• Combining single patient (N-of-1) trials to estimate population treatment effects and to evaluate individual patient responses to treatment

    Item Type Journal Article
    Author D. R. Zucker
    Author C. H. Schmid
    Author M. W. McIntosh
    Author R. B. D'Agostino
    Author H. P. Selker
    Author J. Lau
    Date 1997
    Extra Citation Key: zuc97com tex.citeulike-article-id= 13265083 tex.posted-at= 2014-07-14 14:09:48 tex.priority= 0
    Volume 50
    Pages 401-410
    Publication J Clin Epi
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • hierarchical-model
    • multi-level-model
    • n-of-1-trials
  • The Central Role of Bayes’ Theorem for Joint Estimation of Causal Effects and Propensity Scores

    Item Type Journal Article
    Author Corwin Matthew Zigler
    Abstract Although propensity scores have been central to the estimation of causal effects for over 30 years, only recently has the statistical literature begun to consider in detail methods for Bayesian estimation of propensity scores and causal effects. Underlying this recent body of literature on Bayesian propensity score estimation is an implicit discordance between the goal of the propensity score and the use of Bayes’ theorem. The propensity score condenses multivariate covariate information into a scalar to allow estimation of causal effects without specifying a model for how each covariate relates to the outcome. Avoiding specification of a detailed model for the outcome response surface is valuable for robust estimation of causal effects, but this strategy is at odds with the use of Bayes’ theorem, which presupposes a full probability model for the observed data that adheres to the likelihood principle. The goal of this article is to explicate this fundamental feature of Bayesian estimation of causal effects with propensity scores to provide context for the existing literature and for future work on this important topic.[Received June 2014. Revised September 2015.]
    Date January 2, 2016
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/00031305.2015.1111260
    Accessed 11/25/2019, 7:36:13 AM
    Extra PMID: 27482121
    Volume 70
    Pages 47-54
    Publication The American Statistician
    DOI 10.1080/00031305.2015.1111260
    Issue 1
    ISSN 0003-1305
    Date Added 11/25/2019, 7:36:13 AM
    Modified 11/25/2019, 7:37:12 AM

    Tags:

    • bayes
    • causal-inference
    • causality
    • propensity
  • A Note on Bayesian Inference After Multiple Imputation

    Item Type Journal Article
    Author Xiang Zhou
    Author Jerome P. Reiter
    Abstract This article is aimed at practitioners who plan to use Bayesian inference on multiply-imputed datasets in settings where posterior distributions of the parameters of interest are not approximately Gaussian. We seek to steer practitioners away from a naive approach to Bayesian inference, namely estimating the posterior distribution in each completed dataset and averaging functionals of these distributions. We demonstrate that this approach results in unreliable inferences. A better approach is to mix draws from the posterior distributions from each completed dataset, and use the mixed draws to summarize the posterior distribution. Using simulations, we show that for this second approach to work well, the number of imputed datasets should be large. In particular, five to ten imputed datasets—which is the standard recommendation for multiple imputation—is generally not enough to result in reliable Bayesian inferences.
    Date 2012
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1198/tast.2010.09109
    Accessed 11/10/2023, 9:13:11 AM
    Extra Publisher: Taylor & Francis _eprint: https://doi.org/10.1198/tast.2010.09109
    Volume 64
    Pages 159-163
    Publication The American Statistician
    DOI 10.1198/tast.2010.09109
    Issue 2
    ISSN 0003-1305
    Date Added 11/10/2023, 9:13:11 AM
    Modified 11/10/2023, 9:17:28 AM

    Tags:

    • bayes
    • multiple-imputation
    • imputation
    • missing
    • posterior
  • Frequentist operating characteristics of Bayesian optimal designs via simulation

    Item Type Journal Article
    Author Yifan Zhang
    Author Lorenzo Trippa
    Author Giovanni Parmigiani
    Abstract Bayesian adaptive designs have become popular because of the possibility of increasing the number of patients treated with more beneficial treatments, while still providing sufficient evidence for treatment efficacy comparisons. It can be essential, for regulatory and other purposes, to conduct frequentist analyses both before and after a Bayesian adaptive trial, and these remain challenging. In this paper, we propose a general simulation-based approach to compare frequentist designs with Bayesian adaptive designs based on frequentist criteria such as power and to compute valid frequentist p-values. We illustrate our approach by comparing the power of an equal randomization (ER) design with that of an optimal Bayesian adaptive (OBA) design. The Bayesian design considered here is the dynamic programming solution of the optimization of a specific utility function defined by the number of successes in a patient horizon, including patients whose treatment will be affected by the trial's results after the end of the trial. While the power of an ER design depends on treatment efficacy and the sample size, the power of the OBA design also depends on the patient horizon size. Our results quantify the trade-off between power and the optimal assignment of patients to treatments within the trial. We show that, for large patient horizons, the two criteria are in agreement, while for small horizons, differences can be substantial. This has implications for precision medicine, where patient horizons are decreasing as a result of increasing stratification of patients into subpopulations defined by molecular markers.
    Date 2019
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8279
    Accessed 6/20/2019, 8:01:10 AM
    Rights © 2019 John Wiley & Sons, Ltd.
    Volume 0
    Publication Statistics in Medicine
    DOI 10.1002/sim.8279
    Issue 0
    ISSN 1097-0258
    Date Added 6/20/2019, 8:01:10 AM
    Modified 6/20/2019, 8:02:01 AM

    Tags:

    • bayes
    • simulation-setup
    • simulation
  • Bayesian nonparametric analysis of restricted mean survival time

    Item Type Journal Article
    Author Chenyang Zhang
    Author Guosheng Yin
    Abstract The restricted mean survival time (RMST) evaluates the expectation of survival time truncated by a prespecified time point, because the mean survival time in presence of censoring is typically not estimable. The frequentist inference procedure for RMST has been widely advocated for comparison of two survival curves, while research from the Bayesian perspective is rather limited. For the RMST of both right- and interval-censored data, we propose Bayesian nonparametric estimation and inference procedures. By assigning a mixture of Dirichlet processes (MDP) prior to the distribution function, we can estimate the posterior distribution of RMST. We also explore another Bayesian nonparametric approach using the Dirichlet process mixture model and make comparisons with the frequentist nonparametric method. Simulation studies demonstrate that the Bayesian nonparametric RMST under diffuse MDP priors leads to robust estimation and under informative priors it can incorporate prior knowledge into the nonparametric estimator. Analysis of real trial examples demonstrates the flexibility and interpretability of the Bayesian nonparametric RMST for both right- and interval-censored data. This article is protected by copyright. All rights reserved
    Date 2022
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13622
    Accessed 1/17/2022, 7:37:08 AM
    Volume n/a
    Publication Biometrics
    DOI 10.1111/biom.13622
    Issue n/a
    ISSN 1541-0420
    Date Added 1/17/2022, 7:39:27 AM
    Modified 1/19/2022, 7:46:02 AM

    Tags:

    • bayes
    • survival-analysis
    • non-ph
    • restricted-mean-life
    • nonparametric
  • Clinical trials and sample size considerations: Another perspective

    Item Type Journal Article
    Author Marvin Zelen
    Author Sandra J. Lee
    Date 2000-05
    URL http://dx.doi.org/10.1214/ss/1009212752
    Extra Citation Key: lee00cli tex.citeulike-article-id= 14225799 tex.citeulike-attachment-1= lee00cli.pdf; /pdf/user/harrelfe/article/14225799/1094362/lee00cli.pdf; a9751d588730de4dab642dfd81cf0f58d1e20c21 tex.citeulike-linkout-0= http://dx.doi.org/10.1214/ss/1009212752 tex.posted-at= 2016-12-10 15:43:50 tex.priority= 0
    Volume 15
    Pages 95-110
    Publication Stat Sci
    DOI 10.1214/ss/1009212752
    Issue 2
    ISSN 0883-4237
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • bayesian-inference
    • sample-size

    Notes:

    • uses an unrealistic prior with a point mass at zero effect, discussed in excellent commentaries (e.g., by Rich Simon) which question a few other things

  • Using Bayesian Methods to Augment the Interpretation of Critical Care Trials. An Overview of Theory and Example Reanalysis of the Alveolar Recruitment for Acute Respiratory Distress Syndrome Trial

    Item Type Journal Article
    Author Fernando G. Zampieri
    Author Jonathan D. Casey
    Author Manu Shankar-Hari
    Author Frank E. Harrell
    Author Michael O. Harhay
    Abstract Most randomized trials are designed and analyzed using frequentist statistical approaches such as null hypothesis testing and P values. Conceptually, P values are cumbersome to understand, as they provide evidence of data incompatibility with a null hypothesis (e.g., no clinical benefit) and not direct evidence of the alternative hypothesis (e.g., clinical benefit). This counterintuitive framework may contribute to the misinterpretation that the absence of evidence is equal to evidence of absence and may cause the discounting of potentially informative data. Bayesian methods provide an alternative, probabilistic interpretation of data. The reanalysis of completed trials using Bayesian methods is becoming increasingly common, particularly for trials with effect estimates that appear clinically significant despite P values above the traditional threshold of 0.05. Statistical inference using Bayesian methods produces a distribution of effect sizes that would be compatible with observed trial data, interpreted in the context of prior assumptions about an intervention (called “priors”). These priors are chosen by investigators to reflect existing beliefs and past empirical evidence regarding the effect of an intervention. By calculating the likelihood of clinical benefit, a Bayesian reanalysis can augment the interpretation of a trial. However, if priors are not defined a priori, there is a legitimate concern that priors could be constructed in a manner that produces biased results. Therefore, some standardization of priors for Bayesian reanalysis of clinical trials may be desirable for the critical care community. In this Critical Care Perspective, we discuss both frequentist and Bayesian approaches to clinical trial analysis, introduce a framework that researchers can use to select priors for a Bayesian reanalysis, and demonstrate how to apply our proposal by conducting a novel Bayesian trial reanalysis.
    Date December 3, 2020
    Library Catalog atsjournals.org (Atypon)
    URL https://www.atsjournals.org/doi/10.1164/rccm.202006-2381CP
    Accessed 3/1/2021, 2:54:29 PM
    Extra Publisher: American Thoracic Society - AJRCCM
    Volume 203
    Pages 543-552
    Publication American Journal of Respiratory and Critical Care Medicine
    DOI 10.1164/rccm.202006-2381CP
    Issue 5
    Journal Abbr Am J Respir Crit Care Med
    ISSN 1073-449X
    Date Added 3/1/2021, 2:54:29 PM
    Modified 3/1/2021, 2:55:01 PM

    Tags:

    • bayes
    • rct
    • teaching-mds
    • basic
  • A Bayesian approach in design and analysis of pediatric cancer clinical trials

    Item Type Web Page
    Author Jingjing Ye
    Author Gregory Reaman
    Author R Angelo De Claro
    Author Rajeshwari Sridhara
    Abstract It is well recognized that cancer drug development for children and adolescents has many challenges, from biological and societal to economic. Pediatric cancer consists of a diverse group of rare diseases, and the relatively small population of children with multiple, disparate tumor types across various age groups presents a significant challenge for drug development programs as compared to oncology drug development programs for adults. Due to the different types of cancers, limited opportunities exist for extrapolation of efficacy from adult cancer indications to children. Thus, innovative study designs including Bayesian statistical approaches should be considered. A Bayesian approach can be a flexible tool to formally leverage prior knowledge of adult or external controls in pediatric cancer trials. In this article, we provide in a case example of how Bayesian approaches can be used to design, monitor, and analyze pediatric trials. Particularly, Bayesian sequential monitoring can be useful to monitor pediatric trial results as data accumulate. In addition, designing a pediatric trial with both skeptical and enthusiastic priors with Bayesian sequential monitoring can be an efficient mechanism for early trial cessation for both efficacy and futility. The interpretation of efficacy using a Bayesian approach is based on posterior probability and is intuitive and interpretable for patients, parents and prescribers given limited data.
    Date 2020
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2039?af=R
    Accessed 6/21/2020, 6:42:08 AM
    Date Added 6/21/2020, 6:42:08 AM
    Modified 6/21/2020, 6:50:15 AM

    Tags:

    • bayes
    • prior
    • prior-elicitation
    • pediatric
    • borrow-information
  • Multiple imputation for longitudinal data using Bayesian lasso imputation model

    Item Type Journal Article
    Author Yusuke Yamaguchi
    Author Satoshi Yoshida
    Author Toshihiro Misumi
    Author Kazushi Maruo
    Abstract Multiple imputation is a promising approach to handle missing data and is widely used in analysis of longitudinal clinical studies. A key consideration in the implementation of multiple imputation is to obtain accurate imputed values by specifying an imputation model that incorporates auxiliary variables potentially associated with missing variables. The use of informative auxiliary variables is known to be beneficial to make the missing at random assumption more plausible and help to reduce uncertainty of the imputations; however, it is not straightforward to pre-specify them in many cases. We propose a data-driven specification of the imputation model using Bayesian lasso in the context of longitudinal clinical study, and develop a built-in function of the Bayesian lasso imputation model which is performed within the framework of multiple imputation using chained equations. A simulation study suggested that the Bayesian lasso imputation model worked well in a variety of longitudinal study settings, providing unbiased treatment effect estimates with well-controlled type I error rates and coverage probabilities of the confidence interval; in contrast, ignorance of the informative auxiliary variables led to serious bias and inflation of type I error rate. Moreover, the Bayesian lasso imputation model offered higher statistical powers compared with conventional imputation methods. In our simulation study, the gains in statistical power were remarkable when the sample size was small relative to the number of auxiliary variables. An illustration through a real example also suggested that the Bayesian lasso imputation model could give smaller standard errors of the treatment effect estimate.
    Date 2022
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9315
    Accessed 1/22/2022, 10:23:28 AM
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9315
    Volume n/a
    Publication Statistics in Medicine
    DOI 10.1002/sim.9315
    Issue n/a
    ISSN 1097-0258
    Date Added 1/22/2022, 10:23:28 AM
    Modified 1/22/2022, 10:24:18 AM

    Tags:

    • longitudinal
    • bayes
    • multiple-imputation
    • lasso
    • serial
    • imputatation
  • Bayesian Nonparametric Estimation for Dynamic Treatment Regimes With Sequential Transition Times

    Item Type Journal Article
    Author Yanxun Xu
    Author Peter Müller
    Author Abdus S. Wahed
    Author Peter F. Thall
    Abstract We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 × 2 factorial for frontline therapies only. Motivated by the idea that subsequent salvage treatments affect survival time, we model therapy as a dynamic treatment regime (DTR), that is, an alternating sequence of adaptive treatments or other actions and transition times between disease states. These sequences may vary substantially between patients, depending on how the regime plays out. To evaluate the regimes, mean overall survival time is expressed as a weighted average of the means of all possible sums of successive transitions times. We assume a Bayesian nonparametric survival regression model for each transition time, with a dependent Dirichlet process prior and Gaussian process base measure (DDP-GP). Posterior simulation is implemented by Markov chain Monte Carlo (MCMC) sampling. We provide general guidelines for constructing a prior using empirical Bayes methods. The proposed approach is compared with inverse probability of treatment weighting, including a doubly robust augmented version of this approach, for both single-stage and multi-stage regimes with treatment assignment depending on baseline covariates. The simulations show that the proposed nonparametric Bayesian approach can substantially improve inference compared to existing methods. An R program for implementing the DDP-GP-based Bayesian nonparametric analysis is freely available at www.ams.jhu.edu/yxu70. Supplementary materials for this article are available online.
    Date 2016-07-02
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/01621459.2015.1086353
    Accessed 7/11/2023, 9:52:33 AM
    Extra Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/01621459.2015.1086353 PMID: 28018015
    Volume 111
    Pages 921-950
    Publication Journal of the American Statistical Association
    DOI 10.1080/01621459.2015.1086353
    Issue 515
    ISSN 0162-1459
    Date Added 7/11/2023, 9:52:33 AM
    Modified 7/11/2023, 9:53:23 AM

    Tags:

    • bayes
    • inverse-probability-weight
    • double-robustness
    • dynamic-treatment
  • Bayesian methods to overcome the winner's curse in genetic studies

    Item Type Journal Article
    Author Radu V. Xu
    Author Lei Sun
    Date 2011
    URL http://dx.doi.org/10.1214/10-AOAS373
    Extra Citation Key: xu11bay tex.citeulike-article-id= 13265892 tex.citeulike-linkout-0= http://dx.doi.org/10.1214/10-AOAS373 tex.posted-at= 2014-07-14 14:10:06 tex.priority= 0
    Volume 5
    Pages 201-231
    Publication Ann Appl Stat
    DOI 10.1214/10-AOAS373
    Issue 1
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • shrinkage
    • multiplicity
    • bayesian-model-averaging
    • gene-association-study
    • hierarchical-bayes-model
    • spike-and-slab-prior
    • winners-curse
  • Model Interpretation Through Lower-Dimensional Posterior Summarization

    Item Type Journal Article
    Author Spencer Woody
    Author Carlos M. Carvalho
    Author Jared S. Murray
    Abstract Nonparametric regression models have recently surged in their power and popularity, accompanying the trend of increasing dataset size and complexity. While these models have proven their predictive ability in empirical settings, they are often difficult to interpret and do not address the underlying inferential goals of the analyst or decision maker. In this article, we propose a modular two-stage approach for creating parsimonious, interpretable summaries of complex models which allow freedom in the choice of modeling technique and the inferential target. In the first stage, a flexible model is fit which is believed to be as accurate as possible. In the second stage, lower-dimensional summaries are constructed by projecting draws from the distribution onto simpler structures. These summaries naturally come with valid Bayesian uncertainty estimates. Further, since we use the data only once to move from prior to posterior, these uncertainty estimates remain valid across multiple summaries and after iteratively refining a summary. We apply our method and demonstrate its strengths across a range of simulated and real datasets. The methods we present here are implemented in an R package available at github.com/spencerwoody/possum. Supplementary materials for this article are available online.
    Date July 21, 2020
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/10618600.2020.1796684
    Accessed 8/26/2020, 4:40:14 PM
    Extra Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10618600.2020.1796684
    Volume 0
    Pages 1-9
    Publication Journal of Computational and Graphical Statistics
    DOI 10.1080/10618600.2020.1796684
    Issue 0
    ISSN 1061-8600
    Date Added 8/26/2020, 4:40:17 PM
    Modified 8/26/2020, 4:40:54 PM

    Tags:

    • bayes
    • model-approximation
  • Bayesian statistical inference enhances the interpretation of contemporary randomized controlled trials

    Item Type Journal Article
    Author Duminda N. Wijeysundera
    Author Peter C. Austin
    Author Janet E. Hux
    Author W. Scott Beattie
    Author Andreas Laupacis
    Date 2009
    Extra Citation Key: wij09bay tex.citeulike-article-id= 13265722 tex.posted-at= 2014-07-14 14:10:02 tex.priority= 0
    Volume 62
    Pages 13-21
    Publication J Clin Epi
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • bayesian-inference
    • evidence-based-medicine
    • systematic-reviews

    Notes:

    • Bayesian re-analysis of trials analyzed using frequentist methods

  • Quantification of Prior Impact in Terms of Effective Current Sample Size

    Item Type Journal Article
    Author Manuel Wiesenfarth
    Author Silvia Calderazzo
    Abstract Bayesian methods allow borrowing of historical information through prior distributions. The concept of prior effective sample size (prior ESS) facilitates quantification and communication of such prior information by equating it to a sample size. Prior information can arise from historical observations, thus the traditional approach identifies the ESS with such historical sample size. However, this measure is independent from newly observed data, and thus would not capture an actual “loss of information” induced by the prior in case of prior-data conflict. We build on recent work to relate prior impact to a number of (virtual) samples from the current data model and introduce the effective current sample size (ECSS) of a prior, tailored to the application in Bayesian clinical trial designs. Special emphasis is put on robust mixture, power and commensurate priors. We apply the approach to an adaptive design in which the number of recruited patients is adjusted depending on the effective sample size at an interim analysis. We argue that the ECSS is the appropriate measure in this case, as the aim is to save current (as opposed to historical) patients from recruitment. Furthermore, the ECSS can help overcoming lack of consensus in the ESS assessment of mixture priors and can, more broadly, provide further insights into the impact of priors. An R package accompanies the paper. This article is protected by copyright. All rights reserved.
    Date 2019
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13124
    Accessed 7/31/2019, 3:34:40 PM
    Rights This article is protected by copyright. All rights reserved.
    Volume 0
    Publication Biometrics
    DOI 10.1111/biom.13124
    Issue ja
    ISSN 1541-0420
    Date Added 7/31/2019, 3:34:40 PM
    Modified 7/31/2019, 3:36:43 PM

    Tags:

    • bayes
    • sample-size
    • prior
  • Bayesian sample sizes for exploratory clinical trials comparing multiple experimental treatments with a control

    Item Type Journal Article
    Author John Whitehead
    Author Faye Cleary
    Author Amanda Turner
    Date 2015-05
    URL http://dx.doi.org/10.1002/sim.6469
    Extra Citation Key: whi15bay tex.citeulike-article-id= 14214859 tex.citeulike-attachment-1= whi15bay.pdf; /pdf/user/harrelfe/article/14214859/1092995/whi15bay.pdf; dbda88eb5417652c66231374ae636e7eed8f1d72 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.6469 tex.day= 30 tex.posted-at= 2016-11-25 19:08:14 tex.priority= 0
    Volume 34
    Pages 2048-2061
    Publication Stat Med
    DOI 10.1002/sim.6469
    Issue 12
    ISSN 02776715
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • bayesian-inference
    • sample-size
    • bayesian-methods
    • bayesian-sample-size-estimation
  • A Bayesian perspective on the Bonferroni adjustment

    Item Type Journal Article
    Author Peter H. Westfall
    Author Wesley O. Johnson
    Author Jessica M. Utts
    Date 1997
    Extra Citation Key: wes97bay tex.citeulike-article-id= 13265041 tex.posted-at= 2014-07-14 14:09:48 tex.priority= 0
    Volume 84
    Pages 419-427
    Publication Biometrika
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • multiple-comparisons
    • multiplicity

    Notes:

    • Bonferroni adjustment is consistent with prior which assumes that the probability that all null hypotheses is true is a constant (say 0.5) no matter how many hypotheses are tested; If priors for individual parameters are well calibrated there is no need for adjusting the prior to take into account other hypotheses being tested

  • The influence of variable selection: A Bayesian diagnostic perspective

    Item Type Journal Article
    Author Robert E. Weiss
    Date 1995
    Extra Citation Key: wei95inf tex.citeulike-article-id= 13265035 tex.posted-at= 2014-07-14 14:09:48 tex.priority= 0
    Volume 90
    Pages 619-625
    Publication J Am Stat Assoc
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • variable-selection
    • influence-of-adjustment-on-point-estimates-of-effects
    • logistic-simulation-setup
  • Sample size determination for Bayesian analysis of small n sequential, multiple assignment, randomized trials (snSMARTs) with three agents

    Item Type Journal Article
    Author Boxian Wei
    Author Thomas M. Braun
    Author Roy N. Tamura
    Author Kelley Kidwell
    Abstract The small n, Sequential, Multiple Assignment, Randomized Trial (snSMART) is a two-stage clinical trial design for rare diseases motivated by the comparison of three active treatments for isolated skin vasculitis in the ongoing clinical trial ARAMIS (a randomized multicenter study for isolated skin vasculitis, NCT09239573). In Stage 1, all patients are randomized to one of three treatments. In Stage 2, patients who respond to their initial treatment receive the same treatment again, while those who fail to respond are re-randomized to one of the two remaining treatments. A Bayesian method for estimating the response rate of each individual treatment in a three-arm snSMART demonstrated efficiency gains for a given sample size relative to other existing frequentist approaches. However, these efficiency gains are dependent upon knowing how many subjects are required to determine a specific difference in the treatment response rates. Because few sample size calculation methods for snSMARTs exist, we propose a Bayesian sample size calculation for an snSMART designed to distinguish the best treatment from the second-best treatment. Although our methods are based on asymptotic approximations, we demonstrate via simulations that our proposed sample size calculation approach produces the desired statistical power, even in small samples. Moreover, our methods and applet produce sample sizes quickly, thereby saving time relative to using simulations to determine the appropriate sample size. We compare our proposed sample size to an existing frequentist method based upon a weighted Z -statistic and demonstrate that the Bayesian method requires far fewer patients than the frequentist method for a study with the same design parameters.
    Date September 6, 2020
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/10543406.2020.1815032
    Accessed 9/12/2020, 2:01:11 PM
    Extra Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10543406.2020.1815032 PMID: 32892710
    Volume 0
    Pages 1-12
    Publication Journal of Biopharmaceutical Statistics
    DOI 10.1080/10543406.2020.1815032
    Issue 0
    ISSN 1054-3406
    Date Added 9/12/2020, 2:01:11 PM
    Modified 9/12/2020, 2:01:52 PM

    Tags:

    • bayes
    • sample-size
    • adaptive
    • smart
  • How to use prior knowledge and still give new data a chance?

    Item Type Journal Article
    Author Kristina Weber
    Author Rob Hemmings
    Author Armin Koch
    Abstract A common challenge for the development of drugs in rare diseases and special populations, eg, paediatrics, is the small numbers of patients that can be recruited into clinical trials. Extrapolation can be used to support development and licensing in paediatrics through the structured integration of available data in adults and prospectively generated data in paediatrics to derive conclusions that support licensing decisions in the target paediatric population. In this context, Bayesian analyses have been proposed to obtain formal proof of efficacy of a new drug or therapeutic principle by using additional information (data, opinion, or expectation), expressed through a prior distribution. However, little is said about the impact of the prior assumptions on the evaluation of outcome and prespecified strategies for decision-making as required in the regulatory context. On the basis of examples, we explore the use of data-based Bayesian meta-analytic–predictive methods and compare these approaches with common frequentist and Bayesian meta-analysis models. Noninformative efficacy prior distributions usually do not change the conclusions irrespective of the chosen analysis method. However, if heterogeneity is considered, conclusions are highly dependent on the heterogeneity prior. When using informative efficacy priors based on previous study data in combination with heterogeneity priors, these may completely determine conclusions irrespective of the data generated in the target population. Thus, it is important to understand the impact of the prior assumptions and ensure that prospective trial data in the target population have an appropriate chance, to change prior belief to avoid trivial and potentially erroneous conclusions.
    Date 2018
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1862
    Accessed 7/13/2018, 11:34:41 AM
    Rights © 2018 The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd.
    Volume 17
    Pages 329-341
    Publication Pharmaceutical Statistics
    DOI 10.1002/pst.1862
    Issue 4
    ISSN 1539-1612
    Date Added 7/13/2018, 11:34:41 AM
    Modified 7/13/2018, 11:35:37 AM

    Tags:

    • bayes
    • prior
    • historical-data
    • drug-development
    • pediatric
  • A Bayesian Approach on Sample Size Calculation for Comparing Means

    Item Type Journal Article
    Author Hansheng Wang
    Author Shein-Chung Chow
    Author Murphy Chen
    Date 2005-09
    URL http://dx.doi.org/10.1081/bip-200067789
    Extra Citation Key: wan05bay tex.citeulike-article-id= 14259681 tex.citeulike-attachment-1= wan05bay.pdf; /pdf/user/harrelfe/article/14259681/1098761/wan05bay.pdf; ad82619a73d7171f45bbc0f6a50c14c929071852 tex.citeulike-linkout-0= http://dx.doi.org/10.1081/bip-200067789 tex.day= 1 tex.posted-at= 2017-01-21 20:16:54 tex.priority= 2
    Volume 15
    Pages 799-807
    Publication J Biopharm Stat
    DOI 10.1081/bip-200067789
    Issue 5
    ISSN 1054-3406
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • sample-size
    • bayesian-sample-size-estimation

    Notes:

    • analytic form for posterior for normal t-test case

  • A simple new approach to variable selection in regression, with application to genetic fine mapping

    Item Type Journal Article
    Author Gao Wang
    Author Abhishek Sarkar
    Author Peter Carbonetto
    Author Matthew Stephens
    Abstract We introduce a simple new approach to variable selection in linear regression, with a particular focus on quantifying uncertainty in which variables should be selected. The approach is based on a new model—the ‘sum of single effects’ model, called ‘SuSiE’—which comes from writing the sparse vector of regression coefficients as a sum of ‘single-effect’ vectors, each with one non-zero element. We also introduce a corresponding new fitting procedure—iterative Bayesian stepwise selection (IBSS)—which is a Bayesian analogue of stepwise selection methods. IBSS shares the computational simplicity and speed of traditional stepwise methods but, instead of selecting a single variable at each step, IBSS computes a distribution on variables that captures uncertainty in which variable to select. We provide a formal justification of this intuitive algorithm by showing that it optimizes a variational approximation to the posterior distribution under SuSiE. Further, this approximate posterior distribution naturally yields convenient novel summaries of uncertainty in variable selection, providing a credible set of variables for each selection. Our methods are particularly well suited to settings where variables are highly correlated and detectable effects are sparse, both of which are characteristics of genetic fine mapping applications. We demonstrate through numerical experiments that our methods outperform existing methods for this task, and we illustrate their application to fine mapping genetic variants influencing alternative splicing in human cell lines. We also discuss the potential and challenges for applying these methods to generic variable-selection problems.
    Date 2020
    Language en
    Library Catalog Wiley Online Library
    URL https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssb.12388
    Accessed 12/8/2020, 1:53:12 PM
    Rights © 2020 The Authors Journal of the Royal Statistical Society: Series B (Statistical Methodology) Published by John Wiley & Sons Ltd on behalf of Royal Statistical Society.
    Extra _eprint: https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/rssb.12388
    Volume 82
    Pages 1273-1300
    Publication Journal of the Royal Statistical Society: Series B (Statistical Methodology)
    DOI https://doi.org/10.1111/rssb.12388
    Issue 5
    ISSN 1467-9868
    Date Added 12/8/2020, 1:53:12 PM
    Modified 12/8/2020, 1:54:02 PM

    Tags:

    • bayes
    • variable-selection
    • rms
  • Inference for smooth curves in longitudinal data with application to an AIDS clinical trial

    Item Type Journal Article
    Author Yongxiao Wang
    Author Jeremy M. G. Taylor
    Date 1995
    URL http://dx.doi.org/10.1002/sim.4780141106
    Extra Citation Key: wan95inf tex.citeulike-article-id= 13265022 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.4780141106 tex.posted-at= 2014-07-14 14:09:47 tex.priority= 0
    Volume 14
    Pages 1205-1218
    Publication Stat Med
    DOI 10.1002/sim.4780141106
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • bayesian-inference
    • serial-data
    • shrinkage
    • penalized-mle
    • pmle
  • Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications

    Item Type Journal Article
    Author Eric-Jan Wagenmakers
    Author Maarten Marsman
    Author Tahira Jamil
    Author Alexander Ly
    Author Josine Verhagen
    Author Jonathon Love
    Author Ravi Selker
    Author Quentin F. Gronau
    Author Martin ̌Sḿıra
    Author Sacha Epskamp
    Author Dora Matzke
    Author Jeffrey N. Rouder
    Author Richard D. Morey
    Date 2017
    URL http://dx.doi.org/10.3758/s13423-017-1343-3
    Extra Citation Key: wag17bay1 tex.booktitle= Psychonomic Bulletin & Review tex.citeulike-article-id= 14438461 tex.citeulike-linkout-0= http://dx.doi.org/10.3758/s13423-017-1343-3 tex.citeulike-linkout-1= http://link.springer.com/article/10.3758/s13423-017-1343-3 tex.posted-at= 2017-09-26 18:41:53 tex.priority= 0 tex.publisher= Springer US
    Pages 1-23
    DOI 10.3758/s13423-017-1343-3
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • excellent-for-teaching-bayesian-methods-and-explaining-the-advantages
  • On the treatment effect heterogeneity of antidepressants in major depression: A Bayesian meta-analysis and simulation study

    Item Type Journal Article
    Author Constantin Volkmann
    Author Alexander Volkmann
    Author Christian A. Müller
    Abstract Background The average treatment effect of antidepressants in major depression was found to be about 2 points on the 17-item Hamilton Depression Rating Scale, which lies below clinical relevance. Here, we searched for evidence of a relevant treatment effect heterogeneity that could justify the usage of antidepressants despite their low average treatment effect. Methods Bayesian meta-analysis of 169 randomized, controlled trials including 58,687 patients. We considered the effect sizes log variability ratio (lnVR) and log coefficient of variation ratio (lnCVR) to analyze the difference in variability of active and placebo response. We used Bayesian random-effects meta-analyses (REMA) for lnVR and lnCVR and fitted a random-effects meta-regression (REMR) model to estimate the treatment effect variability between antidepressants and placebo. Results The variability ratio was found to be very close to 1 in the best fitting models (REMR: 95% highest density interval (HDI) [0.98, 1.02], REMA: 95% HDI [1.00, 1.02]). The between-study standard deviation τ under the REMA with respect to lnVR was found to be low (95% HDI [0.00, 0.02]). Simulations showed that a large treatment effect heterogeneity is only compatible with the data if a strong correlation between placebo response and individual treatment effect is assumed. Conclusions The published data from RCTs on antidepressants for the treatment of major depression is compatible with a near-constant treatment effect. Although it is impossible to rule out a substantial treatment effect heterogeneity, its existence seems rather unlikely. Since the average treatment effect of antidepressants falls short of clinical relevance, the current prescribing practice should be re-evaluated.
    Date Nov 11, 2020
    Language en
    Short Title On the treatment effect heterogeneity of antidepressants in major depression
    Library Catalog PLoS Journals
    URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241497
    Accessed 11/11/2020, 3:00:03 PM
    Extra Publisher: Public Library of Science
    Volume 15
    Pages e0241497
    Publication PLOS ONE
    DOI 10.1371/journal.pone.0241497
    Issue 11
    Journal Abbr PLOS ONE
    ISSN 1932-6203
    Date Added 11/11/2020, 3:00:06 PM
    Modified 11/11/2020, 3:00:48 PM

    Tags:

    • bayes
    • hte
    • depression
  • Bayesian model averaging in proportional hazard models: Assessing the risk of a stroke

    Item Type Journal Article
    Author Chris T. Volinsky
    Author David Madigan
    Author Adrian E. Raftery
    Author Richard A. Kronmal
    Date 1997
    Extra Citation Key: vol97bay tex.citeulike-article-id= 13265006 tex.posted-at= 2014-07-14 14:09:47 tex.priority= 0
    Volume 46
    Pages 433-448
    Publication Appl Stat
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • model-uncertainty
    • bayesian-methods
    • variable-selection
    • cox-ph-model
    • model-averaging
    • partial-predictive-score
  • Bayesian Uncertainty Directed Trial Designs

    Item Type Journal Article
    Author Steffen Ventz
    Author Matteo Cellamare
    Author Sergio Bacallado
    Author Lorenzo Trippa
    Abstract Most Bayesian response-adaptive designs unbalance randomization rates toward the most promising arms with the goal of increasing the number of positive treatment outcomes during the study, even though the primary aim of the trial is different. We discuss Bayesian uncertainty directed designs (BUD), a class of Bayesian designs in which the investigator specifies an information measure tailored to the experiment. All decisions during the trial are selected to optimize the available information at the end of the study. The approach can be applied to several designs, ranging from early stage multi-arm trials to biomarker-driven and multi-endpoint studies. We discuss the asymptotic limit of the patient allocation proportion to treatments, and illustrate the finite-sample operating characteristics of BUD designs through examples, including multi-arm trials, biomarker-stratified trials, and trials with multiple co-primary endpoints. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
    Date July 3, 2019
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/01621459.2018.1497497
    Accessed 12/16/2019, 7:52:46 AM
    Volume 114
    Pages 962-974
    Publication Journal of the American Statistical Association
    DOI 10.1080/01621459.2018.1497497
    Issue 527
    ISSN 0162-1459
    Date Added 12/16/2019, 7:52:46 AM
    Modified 12/16/2019, 7:53:29 AM

    Tags:

    • bayes
    • adaptive
    • experimental-design
  • Special issue on cluster randomized trials

    Item Type Journal Article
    Author Various
    Date 2001
    Extra Citation Key: cluster.rct tex.citeulike-article-id= 13265182 tex.posted-at= 2014-07-14 14:09:51 tex.priority= 0
    Volume 20
    Publication Stat Med
    Issue 3
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • sample-size
    • bayesian-model
    • cluster-randomized-trials
    • design
  • Construction, validation and updating of a prognostic model for kidney graft survival

    Item Type Journal Article
    Author Hans C. van Houwelingen
    Author Jane Thorogood
    Date 1995
    Extra Citation Key: hou95con tex.citeulike-article-id= 13264331 tex.posted-at= 2014-07-14 14:09:33 tex.priority= 0
    Volume 14
    Pages 1999-2008
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • validation
    • random-effects
    • variable-selection
    • calibration
    • overfitting
    • shrinkage
    • empirical-bayes
    • parametric-survival-model
  • Effect of Intravenous or Intraosseous Calcium vs Saline on Return of Spontaneous Circulation in Adults With Out-of-Hospital Cardiac Arrest: A Randomized Clinical Trial

    Item Type Journal Article
    Author Mikael Fink Vallentin
    Author Asger Granfeldt
    Author Carsten Meilandt
    Author Amalie Ling Povlsen
    Author Birthe Sindberg
    Author Mathias J. Holmberg
    Author Bo Nees Iversen
    Author Rikke Mærkedahl
    Author Lone Riis Mortensen
    Author Rasmus Nyboe
    Author Mads Partridge Vandborg
    Author Maren Tarpgaard
    Author Charlotte Runge
    Author Christian Fynbo Christiansen
    Author Thomas H. Dissing
    Author Christian Juhl Terkelsen
    Author Steffen Christensen
    Author Hans Kirkegaard
    Author Lars W. Andersen
    Abstract It is unclear whether administration of calcium has a beneficial effect in patients with cardiac arrest.To determine whether administration of calcium during out-of-hospital cardiac arrest improves return of spontaneous circulation in adults.This double-blind, placebo-controlled randomized clinical trial included 397 adult patients with out-of-hospital cardiac arrest and was conducted in the Central Denmark Region between January 20, 2020, and April 15, 2021. The last 90-day follow-up was on July 15, 2021.The intervention consisted of up to 2 intravenous or intraosseous doses with 5 mmol of calcium chloride (n = 197) or saline (n = 200). The first dose was administered immediately after the first dose of epinephrine.The primary outcome was sustained return of spontaneous circulation. The secondary outcomes included survival and a favorable neurological outcome (modified Rankin Scale score of 0-3) at 30 days and 90 days.Based on a planned interim analysis of 383 patients, the steering committee stopped the trial early due to concerns about harm in the calcium group. Of 397 adult patients randomized, 391 were included in the analyses (193 in the calcium group and 198 in the saline group; mean age, 68 [SD, 14] years; 114 [29%] were female). There was no loss to follow-up. There were 37 patients (19%) in the calcium group who had sustained return of spontaneous circulation compared with 53 patients (27%) in the saline group (risk ratio, 0.72 [95% CI, 0.49 to 1.03]; risk difference, −7.6% [95% CI, −16% to 0.8%]; P = .09). At 30 days, 10 patients (5.2%) in the calcium group and 18 patients (9.1%) in the saline group were alive (risk ratio, 0.57 [95% CI, 0.27 to 1.18]; risk difference, −3.9% [95% CI, −9.4% to 1.3%]; P = .17). A favorable neurological outcome at 30 days was observed in 7 patients (3.6%) in the calcium group and in 15 patients (7.6%) in the saline group (risk ratio, 0.48 [95% CI, 0.20 to 1.12]; risk difference, −4.0% [95% CI, −8.9% to 0.7%]; P = .12). Among the patients with calcium values measured who had return of spontaneous circulation, 26 (74%) in the calcium group and 1 (2%) in the saline group had hypercalcemia.Among adults with out-of-hospital cardiac arrest, treatment with intravenous or intraosseous calcium compared with saline did not significantly improve sustained return of spontaneous circulation. These results do not support the administration of calcium during out-of-hospital cardiac arrest in adults.ClinicalTrials.gov Identifier: NCT04153435
    Date November 30, 2021
    Short Title Effect of Intravenous or Intraosseous Calcium vs Saline on Return of Spontaneous Circulation in Adults With Out-of-Hospital Cardiac Arrest
    Library Catalog Silverchair
    URL https://doi.org/10.1001/jama.2021.20929
    Accessed 12/2/2021, 11:20:41 AM
    Publication JAMA
    DOI 10.1001/jama.2021.20929
    Journal Abbr JAMA
    ISSN 0098-7484
    Date Added 12/2/2021, 11:20:41 AM
    Modified 12/2/2021, 11:21:29 AM

    Tags:

    • bayes
    • rct
    • teaching-mds
    • rct-interpretation

    Notes:

    • Followed recently published Bayesian re-analysis reporting guideliness of Michael Harhay et al

  • Prospective application of Bayesian monitoring and analysis in an `open' randomized clinical trial

    Item Type Journal Article
    Author A. Vail
    Author J. Hornbuckle
    Author D. J. Spiegelhalter
    Author J. G. Thornton
    Date 2001
    URL http://dx.doi.org/10.1002/sim.1171
    Extra Citation Key: vai01pro tex.citeulike-article-id= 13265253 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.1171 tex.posted-at= 2014-07-14 14:09:52 tex.priority= 0
    Volume 20
    Pages 3777-3787
    Publication Stat Med
    DOI 10.1002/sim.1171
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • bayesian-inference
    • sequential-monitoring
    • monitoring
    • priors

    Notes:

    • use of stylized priors because of substantial variability of prior opinions;interim results released to investigators

  • bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)

    Item Type Journal Article
    Author Nikolaus Umlauf
    Author Nadja Klein
    Author Thorsten Simon
    Author Achim Zeileis
    Abstract <p>Over the last decades, the challenges in applied regression and in predictive modeling have been changing considerably: (1) More flexible regression model specifications are needed as data sizes and available information are steadily increasing, consequently demanding for more powerful computing infrastructure. (2) Full probabilistic models by means of distributional regression - rather than predicting only some underlying individual quantities from the distributions such as means or expectations - is crucial in many applications. (3) Availability of Bayesian inference has gained in importance both as an appealing framework for regularizing or penalizing complex models and estimation therein as well as a natural alternative to classical frequentist inference. However, while there has been a lot of research on all three challenges and the development of corresponding software packages, a modular software implementation that allows to easily combine all three aspects has not yet been available for the general framework of distributional regression. To fill this gap, the R package bamlss is introduced for Bayesian additive models for location, scale, and shape (and beyond) - with the name reflecting the most important distributional quantities (among others) that can be modeled with the software. At the core of the package are algorithms for highly-efficient Bayesian estimation and inference that can be applied to generalized additive models or generalized additive models for location, scale, and shape, or more general distributional regression models. However, its building blocks are designed as "Lego bricks" encompassing various distributions (exponential family, Cox, joint models, etc.), regression terms (linear, splines, random effects, tensor products, spatial fields, etc.), and estimators (MCMC, backfitting, gradient boosting, lasso, etc.). It is demonstrated how these can be easily combined to make classical models more flexible or to create new custom models for specific modeling challenges.</p>
    Date November 30, 2021
    URL https://www.jstatsoft.org/index.php/jss/article/view/v100i04
    Accessed 12/5/2021, 6:00:00 PM
    Extra Section: Articles
    Volume 100
    Pages 1 - 53
    Publication Journal of Statistical Software
    DOI 10.18637/jss.v100.i04
    Issue 4
    Journal Abbr J. Stat. Soft.
    Date Added 12/6/2021, 2:47:40 PM
    Modified 12/6/2021, 2:48:01 PM

    Tags:

    • bayes
    • generalized-additive-model
    • gam
    • smoothers

    Notes:

    • Shows linkage between optimization and sampling; uses optimization to start sampling

  • Highest posterior density credible region and minimum area confidence region: the bivariate case

    Item Type Journal Article
    Author N. Turkkan
    Author T. Pham-Gia
    Date 1997
    Extra Citation Key: tur97hig tex.citeulike-article-id= 13264983 tex.posted-at= 2014-07-14 14:09:47 tex.priority= 0
    Volume 46
    Pages 131-140
    Publication Appl Stat
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • algorithm
    • credible-interval
    • credible-region
    • highest-posterior-density
    • minimum-volume-confidence-region
    • multivariate
  • Bayesian sample size calculations for comparing two strategies in SMART studies

    Item Type Journal Article
    Author Armando Turchetta
    Author Erica E.M. Moodie
    Author David A. Stephens
    Author Sylvie D. Lambert
    Abstract In the management of most chronic conditions characterized by the lack of universally effective treatments, adaptive treatment strategies (ATSs) have grown in popularity as they offer a more individualized approach. As a result, sequential multiple assignment randomized trials (SMARTs) have gained attention as the most suitable clinical trial design to formalize the study of these strategies. While the number of SMARTs has increased in recent years, sample size and design considerations have generally been carried out in frequentist settings. However, standard frequentist formulae require assumptions on interim response rates and variance components. Misspecifying these can lead to incorrect sample size calculations and correspondingly inadequate levels of power. The Bayesian framework offers a straightforward path to alleviate some of these concerns. In this paper, we provide calculations in a Bayesian setting to allow more realistic and robust estimates that account for uncertainty in inputs through the ‘two priors’ approach. Additionally, compared to the standard frequentist formulae, this methodology allows us to rely on fewer assumptions, integrate pre-trial knowledge, and switch the focus from the standardized effect size to the minimal detectable difference. The proposed methodology is evaluated in a thorough simulation study and is implemented to estimate the sample size for a full-scale SMART of an Internet-Based Adaptive Stress Management intervention on cardiovascular disease patients using data from its pilot study conducted in two Canadian provinces. This article is protected by copyright. All rights reserved
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13813
    Accessed 12/14/2022, 9:57:31 AM
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13813
    Volume n/a
    Publication Biometrics
    DOI 10.1111/biom.13813
    Issue n/a
    ISSN 1541-0420
    Date Added 12/14/2022, 9:57:31 AM
    Modified 12/14/2022, 9:58:15 AM

    Tags:

    • bayes
    • sample-size
    • design
    • adaptive
    • design-of-rct
    • smart
  • Latent Ornstein-Uhlenbeck models for Bayesian analysis of multivariate longitudinal categorical responses

    Item Type Journal Article
    Author Trung Dung Tran
    Author Emmanuel Lesaffre
    Author Geert Verbeke
    Author Joke Duyck
    Abstract We propose a Bayesian latent Ornstein-Uhlenbeck (OU) model to analyze unbalanced longitudinal data of binary and ordinal variables, which are manifestations of fewer continuous latent variables. We focus on the evolution of such latent variables when they continuously change over time. Existing approaches are limited to data collected at regular time intervals. Our proposal makes use of an OU process for the latent variables to overcome this limitation. We show that assuming real eigenvalues for the drift matrix of the OU process, as is frequently done in practice, can lead to biased estimates and/or misleading inference when the true process is oscillating. In contrast, our proposal allows for both real and complex eigenvalues. We illustrate our proposed model with a motivating dataset, containing patients with amyotrophic lateral sclerosis disease. We were interested in how bulbar, cervical, and lumbar functions evolve over time.
    Language en
    Library Catalog Wiley Online Library
    URL http://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13292
    Accessed 12/11/2020, 4:05:37 PM
    Rights © 2020 The International Biometric Society
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13292
    Volume n/a
    Publication Biometrics
    DOI https://doi.org/10.1111/biom.13292
    Issue n/a
    ISSN 1541-0420
    Date Added 12/11/2020, 4:05:37 PM
    Modified 12/11/2020, 4:06:14 PM

    Tags:

    • bayes
    • categorical-data
    • serial
    • ornstein-uhlenbeck
  • Noninformative priors for one parameter of many

    Item Type Journal Article
    Author Robert Tibshirani
    Date 1989
    Extra Citation Key: tib89non tex.citeulike-article-id= 13264960 tex.posted-at= 2014-07-14 14:09:46 tex.priority= 0
    Volume 76
    Pages 604-608
    Publication Biometrika
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • choice-of-prior
  • Investigating underlying risk as a source of heterogeneity in meta-analysis

    Item Type Journal Article
    Author Simon G. Thompson
    Author Teresa C. Smith
    Author Stephen J. Sharp
    Date 1997
    Extra Citation Key: tho97inv tex.citeulike-article-id= 13264956 tex.posted-at= 2014-07-14 14:09:46 tex.priority= 0 See letter to the editor 18:110-115, 1999
    Volume 16
    Pages 2741-2758
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • meta-analysis
    • bugs
    • random-effects-model
    • baseline-risk
    • measurement-error
    • regression-to-the-mean
    • severity-of-disease
  • BUGS: A program to perform Bayesian inference using Gibbs sampling

    Item Type Book Section
    Author A. Thomas
    Author D. J. Spiegelhalter
    Author W. R. Gilks
    Editor J. M. Bernardo
    Editor J. O. Berger
    Editor A. P. Dawid
    Editor A. F. M. Smith
    Date 1992
    URL http://www.mrc-bsu.cam.ac.uk/bugs
    Extra Citation Key: bugs tex.citeulike-article-id= 13263827 tex.citeulike-linkout-0= http://www.mrc-bsu.cam.ac.uk/bugs tex.posted-at= 2014-07-14 14:09:23 tex.priority= 0
    Volume 4
    Place Oxford, UK
    Publisher Clarendon Press
    Pages 837-842
    Book Title Bayesian Statistics
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • software
  • Empirical Bayes methods for estimating hospital-specific mortality rates

    Item Type Journal Article
    Author Neal Thomas
    Author Nicholas Longford
    Author John E. Rolph
    Date 1994
    Extra Citation Key: tho94emp tex.citeulike-article-id= 13264953 tex.posted-at= 2014-07-14 14:09:46 tex.priority= 0
    Volume 13
    Pages 889-903
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • cluster-sampling
    • shrinkage
    • logistic-model-extensions
    • empirical-bayes
  • Some extensions and applications of a Bayesian strategy for monitoring multiple outcomes in clinical trials

    Item Type Journal Article
    Author Peter F. Thall
    Author Hsi-Guang Sung
    Date 1998
    Extra Citation Key: tha98som tex.citeulike-article-id= 13264945 tex.posted-at= 2014-07-14 14:09:46 tex.priority= 0
    Volume 17
    Pages 1563-1580
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • multiple-endpoints
    • rct
    • study-design
    • bayesian
    • monitoring-study
  • Statistical Remedies for Medical Researchers

    Item Type Book
    Author Peter F. Thall
    Abstract This book illustrates numerous statistical practices that are commonly used by medical researchers, but which have severe flaws that may not be obvious. For each example, it provides one or more alternative statistical methods that avoid misleading or incorrect inferences being made. The technical level is kept to a minimum to make the book accessible to non-statisticians. At the same time, since many of the examples describe methods used routinely by medical statisticians with formal statistical training, the book appeals to a broad readership in the medical research community.
    Date 2020
    Language en
    Library Catalog www.springer.com
    URL https://www.springer.com/gp/book/9783030437138
    Accessed 1/9/2021, 7:48:50 AM
    Extra DOI: 10.1007/978-3-030-43714-5
    Publisher Springer International Publishing
    ISBN 978-3-030-43713-8
    Series Springer Series in Pharmaceutical Statistics
    Date Added 1/9/2021, 7:48:50 AM
    Modified 1/9/2021, 7:50:00 AM

    Tags:

    • bayes
    • teaching-mds
    • basic
  • Bayesian Inference for the One-Factor Copula Model

    Item Type Journal Article
    Author Ban Kheng Tan
    Author Anastasios Panagiotelis
    Author George Athanasopoulos
    Abstract We develop efficient Bayesian inference for the one-factor copula model with two significant contributions over existing methodologies. First, our approach leads to straightforward inference on dependence parameters and the latent factor; only inference on the former is available under frequentist alternatives. Second, we develop a reversible jump Markov chain Monte Carlo algorithm that averages over models constructed from different bivariate copula building blocks. Our approach accommodates any combination of discrete and continuous margins. Through extensive simulations, we compare the computational and Monte Carlo efficiency of alternative proposed sampling schemes. The preferred algorithm provides reliable inference on parameters, the latent factor, and model space. The potential of the methodology is highlighted in an empirical study of 10 binary measures of socio-economic deprivation collected for 11,463 East Timorese households. The importance of conducting inference on the latent factor is motivated by constructing a poverty index using estimates of the factor. Compared to a linear Gaussian factor model, our model average improves out-of-sample fit. The relationships between the poverty index and observed variables uncovered by our approach are diverse and allow for a richer and more precise understanding of the dependence between overall deprivation and individual measures of well-being.
    Date June 11, 2018
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/10618600.2018.1482765
    Accessed 9/8/2018, 3:13:53 PM
    Volume 0
    Pages 1-19
    Publication Journal of Computational and Graphical Statistics
    DOI 10.1080/10618600.2018.1482765
    Issue 0
    ISSN 1061-8600
    Date Added 9/8/2018, 3:13:53 PM
    Modified 9/8/2018, 3:14:28 PM

    Tags:

    • bayes
    • multiple-endpoints
    • copula
  • Bayesian Inference for the One-Factor Copula Model

    Item Type Journal Article
    Author Ban Kheng Tan
    Author Anastasios Panagiotelis
    Author George Athanasopoulos
    Abstract We develop efficient Bayesian inference for the one-factor copula model with two significant contributions over existing methodologies. First, our approach leads to straightforward inference on dependence parameters and the latent factor; only inference on the former is available under frequentist alternatives. Second, we develop a reversible jump Markov chain Monte Carlo algorithm that averages over models constructed from different bivariate copula building blocks. Our approach accommodates any combination of discrete and continuous margins. Through extensive simulations, we compare the computational and Monte Carlo efficiency of alternative proposed sampling schemes. The preferred algorithm provides reliable inference on parameters, the latent factor, and model space. The potential of the methodology is highlighted in an empirical study of 10 binary measures of socio-economic deprivation collected for 11,463 East Timorese households. The importance of conducting inference on the latent factor is motivated by constructing a poverty index using estimates of the factor. Compared to a linear Gaussian factor model, our model average improves out-of-sample fit. The relationships between the poverty index and observed variables uncovered by our approach are diverse and allow for a richer and more precise understanding of the dependence between overall deprivation and individual measures of well-being.
    Date January 2, 2019
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/10618600.2018.1482765
    Accessed 4/25/2019, 10:03:09 AM
    Volume 28
    Pages 155-173
    Publication Journal of Computational and Graphical Statistics
    DOI 10.1080/10618600.2018.1482765
    Issue 1
    ISSN 1061-8600
    Date Added 4/25/2019, 10:03:09 AM
    Modified 4/25/2019, 10:03:45 AM

    Tags:

    • bayes
    • multiple-endpoints
    • rct
    • copula
  • A Bayesian hierarchical model for multi-level repeated ordinal data: Analysis of oral practice examinations in a large anaesthesiology training programme

    Item Type Journal Article
    Author Ming Tan
    Author Yinsheng Qu
    Author Ed Mascha
    Author Armin Schubert
    Date 1999
    Extra Citation Key: tan99bay tex.citeulike-article-id= 13264930 tex.posted-at= 2014-07-14 14:09:46 tex.priority= 0
    Volume 18
    Pages 1983-1992
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • repeated-measures
    • serial-data
    • ordinal-response
    • bayesian-model
    • ordinal-regression
  • Bayesian dose-finding phase I trial design incorporating historical data from a preceding trial

    Item Type Journal Article
    Author Kentaro Takeda
    Author Satoshi Morita
    Abstract We consider the problem of incorporating historical data from a preceding trial to design and conduct a subsequent dose-finding trial in a possibly different population of patients. In oncology, for example, after a phase I dose-finding trial is completed in Caucasian patients, investigators often conduct a further phase I trial to determine the maximum tolerated dose in Asian patients. This may be due to concerns about possible differences in treatment tolerability between populations. In this study, we propose to adaptively incorporate historical data into prior distributions assumed in a new dose-finding trial. Our proposed approach aims to appropriately borrow strength from a previous trial to improve the maximum tolerated dose determination in another patient population. We define a ” historical-to-current (H-C)” parameter representing the degree of borrowing based on a retrospective analysis of previous trial data. In simulation studies, we examine the operating characteristics of the proposed method in comparison with 3 alternative approaches and assess how the H-C parameter functions across a variety of realistic settings.
    URL http://dx.doi.org/10.1002/pst.1850
    Extra Citation Key: tak18bay tex.citeulike-article-id= 14523898 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/pst.1850 tex.posted-at= 2018-01-26 19:15:59 tex.priority= 2
    Pages n/a
    Publication Pharm Stat
    DOI 10.1002/pst.1850
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • historical-data
    • drug-development
    • dose-response
  • A Bayesian framework for pathway‐guided identification of cancer subgroups by integrating multiple types of genomic data

    Item Type Journal Article
    Author Zequn Sun
    Author Dongjun Chung
    Author Brian Neelon
    Author Andrew Millar‐Wilson
    Author Stephen P. Ethier
    Author Feifei Xiao
    Author Yinan Zheng
    Author Kristin Wallace
    Author Gary Hardiman
    Abstract In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi‐omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene‐level analysis and lack the ability to facilitate biological findings at the pathway‐level. In this article, we propose Bayes‐InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway‐guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes‐InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya‐Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package “INGRID” implementing the proposed approach is currently available in our research group GitHub webpage ( https://dongjunchung.github.io/INGRID/ ).
    Date 2023-12-10
    Language en
    Library Catalog DOI.org (Crossref)
    URL https://onlinelibrary.wiley.com/doi/10.1002/sim.9911
    Accessed 12/9/2023, 11:36:41 AM
    Volume 42
    Pages 5266-5284
    Publication Statistics in Medicine
    DOI 10.1002/sim.9911
    Issue 28
    Journal Abbr Statistics in Medicine
    ISSN 0277-6715, 1097-0258
    Date Added 12/9/2023, 11:36:41 AM
    Modified 12/9/2023, 11:37:43 AM

    Tags:

    • bayes
    • high-dimensional-data
    • multi-omics
  • Prediction and Inference With Missing Data in Patient Alert Systems

    Item Type Journal Article
    Author Curtis B. Storlie
    Author Terry M. Therneau
    Author Rickey E. Carter
    Author Nicholas Chia
    Author John R. Bergquist
    Author Jeanne M. Huddleston
    Author Santiago Romero-Brufau
    Abstract We describe the Bedside Patient Rescue (BPR) project, the goal of which is risk prediction of adverse events for non-intensive care unit patients using ∼100 variables (vitals, lab results, assessments, etc.). There are several missing predictor values for most patients, which in the health sciences is the norm, rather than the exception. A Bayesian approach is presented that addresses many of the shortcomings to standard approaches to missing predictors: (i) treatment of the uncertainty due to imputation is straight-forward in the Bayesian paradigm, (ii) the predictor distribution is flexibly modeled as an infinite normal mixture with latent variables to explicitly account for discrete predictors (i.e., as in multivariate probit regression models), and (iii) certain missing not at random situations can be handled effectively by allowing the indicator of missingness into the predictor distribution only to inform the distribution of the missing variables. The proposed approach also has the benefit of providing a distribution for the prediction, including the uncertainty inherent in the imputation. Therefore, we can ask questions such as: is it possible this individual is at high risk but we are missing too much information to know for sure? How much would we reduce the uncertainty in our risk prediction by obtaining a particular missing value? This approach is applied to the BPR problem resulting in excellent predictive capability to identify deteriorating patients. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
    Date April 23, 2019
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/01621459.2019.1604359
    Accessed 6/20/2019, 7:35:05 AM
    Volume 0
    Pages 1-28
    Publication Journal of the American Statistical Association
    DOI 10.1080/01621459.2019.1604359
    Issue 0
    ISSN 0162-1459
    Date Added 6/20/2019, 7:35:05 AM
    Modified 6/20/2019, 7:37:05 AM

    Tags:

    • bayes
    • prediction
    • missing
    • dynamic-prediction
  • Prediction and decision making using Bayesian hierarchical models

    Item Type Journal Article
    Author Dalene K. Stangl
    Date 1995
    Extra Citation Key: sta95pre tex.citeulike-article-id= 13264901 tex.posted-at= 2014-07-14 14:09:45 tex.priority= 0
    Volume 14
    Pages 2173-2190
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-methods
    • shrinkage
    • bayesian-survival-analysis
    • mixed-model
    • multi-center-study
    • qoi
    • quantity-of-interest
    • random-effects-model
    • site-effects
    • site-variation
  • Analysis of paediatric visual acuity using Bayesian copula models with sinh-arcsinh marginal densities

    Item Type Journal Article
    Author Julian Stander
    Author Luciana Dalla Valle
    Author Charlotte Taglioni
    Author Brunero Liseo
    Author Angie Wade
    Author Mario Cortina‐Borja
    Abstract We analyse paediatric ophthalmic data from a large sample of children aged between 3 and 8 years. We use a Bayesian additive conditional bivariate copula regression model with sinh-arcsinh marginal densities with location, scale, and shape parameters that depend smoothly on a covariate. We perform Bayesian inference about the unknown quantities of our model using a specially tailored Markov chain Monte Carlo algorithm. We gain new insights about the processes, which determine transformations in visual acuity with respect to age, including the nature of joint changes in both eyes as modelled with the age-related copula dependence parameter. We analyse posterior predictive distributions to identify children with unusual sight characteristics, distinguishing those who are bivariate, but not univariate outliers. In this way, we provide an innovative tool that enables clinicians to identify children with unusual sight who may otherwise be missed. We compare our simultaneous Bayesian method with a two-step frequentist generalised additive modelling approach.
    Date 2019
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8176
    Accessed 6/1/2019, 10:10:58 AM
    Rights © 2019 John Wiley & Sons, Ltd.
    Volume 0
    Publication Statistics in Medicine
    DOI 10.1002/sim.8176
    Issue 0
    ISSN 1097-0258
    Date Added 6/1/2019, 10:10:58 AM
    Modified 6/1/2019, 10:11:43 AM

    Tags:

    • bayes
    • copula
  • Stan: A C++ Library for Probability and Sampling

    Item Type Journal Article
    Author Stan Development Team
    Date 2020
    URL https://cran.r-project.org/package=rstan
    Extra Citation Key: rstan tex.citeulike-article-id= 14179501 tex.citeulike-linkout-0= https://cran.r-project.org/package=rstan tex.citeulike-linkout-1= http://mc-stan.org tex.posted-at= 2016-11-08 21:03:13 tex.priority= 2
    Date Added 7/7/2018, 1:38:33 PM
    Modified 8/3/2020, 6:11:50 AM

    Tags:

    • bayesian-inference
    • statistical-computing
    • bayesian-modeling
  • Sample size determination for phase II clinical trials based on Bayesian decision theory

    Item Type Journal Article
    Author Nigel Stallard
    Date 1998
    Extra Citation Key: sta98sam tex.citeulike-article-id= 13264902 tex.posted-at= 2014-07-14 14:09:45 tex.priority= 0
    Volume 54
    Pages 279-294
    Publication Biometrics
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • bayesian-inference
    • study-design
    • sample-size
    • backwards-induction
    • cost-benefit-analysis
    • gain-function-used-to-consider-costs-of-drug-development
    • group-sequential
    • multistage-design
    • optimal-stopping
  • Applying Bayesian ideas in drug development and clinical trials

    Item Type Journal Article
    Author David J. Spiegelhalter
    Author L. S. Freedman
    Author M. K. B. Parmar
    Date 1993
    URL http://dx.doi.org/10.1002/sim.4780121516
    Extra Citation Key: spi93app tex.citeulike-article-id= 13264890 tex.citeulike-attachment-1= spi93app.pdf; /pdf/user/harrelfe/article/13264890/1092998/spi93app.pdf; 4c166454c10614c4d04e5d4d5d206982fd1a9fc4 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.4780121516 tex.posted-at= 2014-07-14 14:09:45 tex.priority= 0
    Volume 12
    Pages 1501-1511
    Publication Stat Med
    DOI 10.1002/sim.4780121516
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • bayesian-inference
    • clinical-trials
  • Bayesian approaches to randomized trials

    Item Type Journal Article
    Author David J. Spiegelhalter
    Author Laurence S. Freedman
    Author Mahesh K. B. Parmar
    Date 1994
    URL https://doi.org/10.2307/2983527
    Extra Citation Key: spi94bay tex.citeulike-article-id= 13264891 tex.posted-at= 2014-07-14 14:09:45 tex.priority= 0
    Volume 157
    Pages 357-416
    Publication J Roy Stat Soc A
    DOI 10.2307/2983527
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • sequential-testing
    • early-termination
    • equivalence
    • interpreting-study-results
    • skeptical-priors
  • A predictive approach to selecting the size of a clinical trial, based on subjective clinical opinion

    Item Type Journal Article
    Author David J. Spiegelhalter
    Author Lawrence S. Freedman
    Date 1986
    URL http://dx.doi.org/10.1002/sim.4780050103
    Extra Citation Key: spi86pre tex.citeulike-article-id= 13264889 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.4780050103 tex.posted-at= 2014-07-14 14:09:45 tex.priority= 0
    Volume 5
    Pages 1-13
    Publication Stat Med
    DOI 10.1002/sim.4780050103
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • bayesian-inference
    • study-design
    • sample-size
    • predictive-distributions
  • Probabilistic prediction in patient management and clinical trials

    Item Type Journal Article
    Author D. J. Spiegelhalter
    Date 1986
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.4780050506
    Extra Citation Key: spi86
    Volume 5
    Pages 421-433
    Publication Stat Med
    DOI 10.1002/sim.4780050506
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • general
    • predictive-accuracy
    • calibration-test
    • idiot-bayes
    • independence-model
    • nonparametric-calibration-curve
    • prediction
    • shrinkage

    Notes:

    • z-test for calibration inaccuracy (implemented in Stata, and R Hmisc package's val.prob function)

  • Bayesian adaptive non-inferiority with safety assessment: Retrospective case study to highlight potential benefits and limitations of the approach

    Item Type Journal Article
    Author Melissa Spann
    Author Stacy Lindborg
    Author John Seaman
    Author Robert Baker
    Author Eduardo Dunayevich
    Author Alan Breier
    Date 2009
    URL http://dx.doi.org/10.1016/j.jpsychires.2008.07.009
    Extra Citation Key: spa09bay tex.citeulike-article-id= 13265734 tex.citeulike-linkout-0= http://dx.doi.org/10.1016/j.jpsychires.2008.07.009 tex.posted-at= 2014-07-14 14:10:03 tex.priority= 0
    Volume 43
    Pages 561-567
    Publication J Psych Res
    DOI 10.1016/j.jpsychires.2008.07.009
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • adaptive
    • bayesian-adaptive-trial
    • joint-predictive-probability
    • non-inferiority
    • schizophrenia

    Notes:

    • adaptation based on product of posterior probabilities of efficacy and not having a certain adverse event;retrospective re-running a finished trial, incorportation adaptive allocation by using only part of the stream of patients in the original order of enrollment;nice background of Bayesian method;used predictive probabilities

  • Applications of Bayesian statistical methodology to clinical trial design: A case study of a phase 2 trial with an interim futility assessment in patients with knee osteoarthritis

    Item Type Journal Article
    Author Claire L. Smith
    Author Yan Jin
    Author Eyas Raddad
    Author Terry A. McNearney
    Author Xiao Ni
    Author David Monteith
    Author Roger Brown
    Author Mark A. Deeg
    Author Thomas Schnitzer
    Abstract Development of new pharmacological treatments for osteoarthritis that address unmet medical needs in a competitive market place is challenging. Bayesian approaches to trial design offer advantages in defining treatment benefits by addressing clinically relevant magnitude of effects relative to comparators and in optimizing efficiency in analysis. Such advantages are illustrated by a motivating case study, a proof of concept, and dose finding study in patients with osteoarthritis. Patients with osteoarthritis were randomized to receive placebo, celecoxib, or 1 of 4 doses of galcanezumab. Primary outcome measure was change from baseline WOMAC pain after 8 weeks of treatment. Literature review of clinical trials with targeted comparator therapies quantified treatment effects versus placebo. Two success criteria were defined: one to address superiority to placebo with adequate precision and another to ensure a clinically relevant treatment effect. Trial simulations used a Bayesian dose response and longitudinal model. An interim analysis for futility was incorporated. Simulations indicated the study had ≥85% power to detect a 14-mm improvement and ≤1% risk for a placebo-like drug to pass. The addition of the second success criterion substantially reduced the risk of an inadequate, weakly efficacious drug proceeding to future development. The study was terminated at the interim analysis due to inadequate analgesic efficacy. A Bayesian approach using probabilistic statements enables clear understanding of success criteria, leading to informed decisions for study conduct. Incorporating an interim analysis can effectively reduce sample size, save resources, and minimize exposure of patients to an inadequate treatment.
    Date 2018
    Language en
    Short Title Applications of Bayesian statistical methodology to clinical trial design
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1906
    Accessed 10/16/2018, 3:02:59 PM
    Rights © 2018 John Wiley & Sons, Ltd.
    Volume 0
    Publication Pharmaceutical Statistics
    DOI 10.1002/pst.1906
    Issue 0
    ISSN 1539-1612
    Date Added 10/16/2018, 3:02:59 PM
    Modified 10/16/2018, 3:03:31 PM

    Tags:

    • bayes
    • rct
    • design
  • Applications of Bayesian statistical methodology to clinical trial design: A case study of a phase 2 trial with an interim futility assessment in patients with knee osteoarthritis

    Item Type Journal Article
    Author Claire L. Smith
    Author Yan Jin
    Author Eyas Raddad
    Author Terry A. McNearney
    Author Xiao Ni
    Author David Monteith
    Author Roger Brown
    Author Mark A. Deeg
    Author Thomas Schnitzer
    Abstract Development of new pharmacological treatments for osteoarthritis that address unmet medical needs in a competitive market place is challenging. Bayesian approaches to trial design offer advantages in defining treatment benefits by addressing clinically relevant magnitude of effects relative to comparators and in optimizing efficiency in analysis. Such advantages are illustrated by a motivating case study, a proof of concept, and dose finding study in patients with osteoarthritis. Patients with osteoarthritis were randomized to receive placebo, celecoxib, or 1 of 4 doses of galcanezumab. Primary outcome measure was change from baseline WOMAC pain after 8 weeks of treatment. Literature review of clinical trials with targeted comparator therapies quantified treatment effects versus placebo. Two success criteria were defined: one to address superiority to placebo with adequate precision and another to ensure a clinically relevant treatment effect. Trial simulations used a Bayesian dose response and longitudinal model. An interim analysis for futility was incorporated. Simulations indicated the study had ≥85% power to detect a 14-mm improvement and ≤1% risk for a placebo-like drug to pass. The addition of the second success criterion substantially reduced the risk of an inadequate, weakly efficacious drug proceeding to future development. The study was terminated at the interim analysis due to inadequate analgesic efficacy. A Bayesian approach using probabilistic statements enables clear understanding of success criteria, leading to informed decisions for study conduct. Incorporating an interim analysis can effectively reduce sample size, save resources, and minimize exposure of patients to an inadequate treatment.
    Date 2019
    Language en
    Short Title Applications of Bayesian statistical methodology to clinical trial design
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1906
    Accessed 1/26/2019, 12:14:39 PM
    Rights © 2018 John Wiley & Sons, Ltd.
    Volume 18
    Pages 39-53
    Publication Pharmaceutical Statistics
    DOI 10.1002/pst.1906
    Issue 1
    ISSN 1539-1612
    Date Added 1/26/2019, 12:14:39 PM
    Modified 1/26/2019, 12:15:14 PM

    Tags:

    • bayes
    • futility
    • drug-development
  • Bayesian approaches to random-effects meta-analysis: A comparative study

    Item Type Journal Article
    Author Teresa A. Smith
    Author David J. Spiegelhalter
    Author Andrew Thomas
    Date 1995
    Extra Citation Key: smi95bay tex.citeulike-article-id= 13264878 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0
    Volume 14
    Pages 2685-2699
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • random-effects
    • meta-analysis
    • bugs
    • uncertainty

    Notes:

    • advantages over DerSimonian and Laird which ignores uncertainty in some parameter estimates

  • Bayes factors and choice criteria for linear models

    Item Type Journal Article
    Author A. F. M. Smith
    Author D. J. Spiegelhalter
    Date 1980
    Extra Citation Key: smi80bay tex.citeulike-article-id= 13264872 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0
    Volume 42
    Pages 213-220
    Publication J Roy Stat Soc B
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • aic
    • bic
    • variable-selection
    • bayes-factor
    • model-selection
    • maximum-likelihood
  • Bayesian statistics without tears: A sampling-resampling perspective

    Item Type Journal Article
    Author A. F. M. Smith
    Author A. E. Gelfand
    Date 1992
    Extra Citation Key: smi92bay tex.citeulike-article-id= 13264874 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0
    Volume 46
    Pages 84-88
    Publication Am Statistician
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • sampling-importance-resampling
    • teaching
    • weighted-bootstrap
    • changing-prior
  • Semiparametric Bayesian analysis of survival data

    Item Type Journal Article
    Author Debajyoti Sinha
    Author Dipak K. Dey
    Date 1997
    Extra Citation Key: sin97sem tex.citeulike-article-id= 13264866 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0
    Volume 92
    Pages 1195-1212
    Publication J Am Stat Assoc
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • survival-models
  • Bayesian optimal stepped wedge design

    Item Type Journal Article
    Author Satya Prakash Singh
    Abstract Abstract Recently, there has been a growing interest in designing cluster trials using stepped wedge design (SWD). An SWD is a type of cluster–crossover design in which clusters of individuals are randomized unidirectional from a control to an intervention at certain time points. The intraclass correlation coefficient (ICC) that measures the dependency of subject within a cluster plays an important role in design and analysis of stepped wedge trials. In this paper, we discuss a Bayesian approach to address the dependency of SWD on the ICC and robust Bayesian SWDs are proposed. Bayesian design is shown to be more robust against the misspecification of the parameter values compared to the locally optimal design. Designs are obtained for the various choices of priors assigned to the ICC. A detailed sensitivity analysis is performed to assess the robustness of proposed optimal designs. The power superiority of Bayesian design against the commonly used balanced design is demonstrated numerically using hypothetical as well as real scenarios.
    Date 2023-12-06
    Language en
    Library Catalog DOI.org (Crossref)
    URL https://onlinelibrary.wiley.com/doi/10.1002/bimj.202300168
    Accessed 12/9/2023, 10:55:57 AM
    Pages 2300168
    Publication Biometrical Journal
    DOI 10.1002/bimj.202300168
    Journal Abbr Biometrical J
    ISSN 0323-3847, 1521-4036
    Date Added 12/9/2023, 10:55:57 AM
    Modified 12/9/2023, 10:56:38 AM

    Tags:

    • bayes
    • stepped-wedge
    • cluster-randomized-trial
  • Bayesian design and analysis of two two factorial clinical trials

    Item Type Journal Article
    Author Richard Simon
    Author Laurence S. Freedman
    Date 1997
    Volume 53
    Pages 456-464
    Publication Biometrics
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/15/2020, 12:26:45 PM

    Tags:

    • bayesian-inference
    • study-design
    • interaction
    • differential-treatment-effect
    • factorial-design
    • interaction-test
    • prior-distribution-for-interaction-effect
  • The Signal and the Noise: Why So Many Predictions Fail--but Some Don't

    Item Type Book
    Author Nate Silver
    Date 2012
    URL http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0143125087
    Extra Citation Key: sil12sig tex.citeulike-article-id= 13675156 tex.citeulike-linkout-0= http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0143125087 tex.citeulike-linkout-1= http://www.amazon.de/exec/obidos/redirect?tag=citeulike01-21&path=ASIN/0143125087 tex.citeulike-linkout-2= http://www.amazon.fr/exec/obidos/redirect?tag=citeulike06-21&path=ASIN/0143125087 tex.citeulike-linkout-3= http://www.amazon.jp/exec/obidos/ASIN/0143125087 tex.citeulike-linkout-4= http://www.amazon.co.uk/exec/obidos/ASIN/0143125087/citeulike00-21 tex.citeulike-linkout-5= http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0143125087 tex.citeulike-linkout-6= http://www.worldcat.org/isbn/0143125087 tex.citeulike-linkout-7= http://books.google.com/books?vid=ISBN0143125087 tex.citeulike-linkout-8= http://www.amazon.com/gp/search?keywords=0143125087&index=books&linkCode=qs tex.citeulike-linkout-9= http://www.librarything.com/isbn/0143125087 tex.howpublished= Paperback tex.posted-at= 2016-11-07 13:36:07 tex.priority= 0
    Publisher Penguin Books
    ISBN 0-14-312508-7
    Edition 1
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • prediction
  • Multiple imputation using an iterative hot-deck with distance-based donor selection

    Item Type Journal Article
    Author Juned Siddique
    Date 2008
    Extra Citation Key: sid08mul tex.citeulike-article-id= 13265659 tex.posted-at= 2014-07-14 14:10:01 tex.priority= 0
    Volume 27
    Pages 83-102
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • missing-data
    • approximate-bayesian-bootstrap
    • distance-weighting
    • implicit-model
    • pmm
    • predictive-mean-matching
  • A Bayesian-bandit adaptive design for N-of-1 clinical trials

    Item Type Journal Article
    Author Sama Shrestha
    Author Sonia Jain
    Abstract N-of-1 trials, which are randomized, double-blinded, controlled, multiperiod, crossover trials on a single subject, have been applied to determine the heterogeneity of the individual's treatment effect in precision medicine settings. An aggregated N-of-1 design, which can estimate the population effect from these individual trials, is a pragmatic alternative when a randomized controlled trial (RCT) is infeasible. We propose a Bayesian adaptive design for both the individual and aggregated N-of-1 trials using a multiarmed bandit framework that is estimated via efficient Markov chain Monte Carlo. A Bayesian hierarchical structure is used to jointly model the individual and population treatment effects. Our proposed adaptive trial design is based on Thompson sampling, which randomly allocates individuals to treatments based on the Bayesian posterior probability of each treatment being optimal. While we use a subject-specific treatment effect and Bayesian posterior probability estimates to determine an individual's treatment allocation, our hierarchical model facilitates these individual estimates to borrow strength from the population estimates via shrinkage to the population mean. We present the design's operating characteristics and performance via a simulation study motivated by a recently completed N-of-1 clinical trial. We demonstrate that from a patient-centered perspective, subjects are likely to benefit from our adaptive design, in particular, for those individuals that deviate from the overall population effect.
    Language en
    Library Catalog Wiley Online Library
    URL http://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8873
    Accessed 1/30/2021, 3:00:53 PM
    Rights © 2021 John Wiley & Sons, Ltd.
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.8873
    Volume n/a
    Publication Statistics in Medicine
    DOI https://doi.org/10.1002/sim.8873
    Issue n/a
    ISSN 1097-0258
    Date Added 1/30/2021, 3:00:53 PM
    Modified 1/30/2021, 3:01:29 PM

    Tags:

    • bayes
    • adaptive
    • n-of-1-trial
  • The intellectual health of clinical drug evaluation

    Item Type Journal Article
    Author Lewis B. Sheiner
    Date 1991
    URL http://dx.doi.org/10.1038/clpt.1991.97
    Extra Citation Key: she91int tex.citeulike-article-id= 13264842 tex.citeulike-linkout-0= http://dx.doi.org/10.1038/clpt.1991.97 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0
    Volume 50
    Pages 4-9
    Publication Clin Pharm Ther
    DOI 10.1038/clpt.1991.97
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • bayesian-inference
    • teaching-mds
    • statistical-significance
    • reporting
    • clinical-trials
    • compliance
    • review
    • hypothesis-testing

    Notes:

    • problems with traditional statistical approaches to drug evaluation;problems with under-emphasis of type II error

  • Commentary: Learning versus confirming in clinical drug development

    Item Type Journal Article
    Author Lewis B. Sheiner
    Date 1997
    Extra Citation Key: she97lea tex.citeulike-article-id= 13264844 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0
    Volume 61
    Pages 275-291
    Publication Clin Pharm Ther
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • study-design
    • bayesian-modeling
    • drug-development-program

    Notes:

    • need for modeling in RCT to interpret complex quantitative experience;response surface;dose-finding;dose-ranging;confirmatory vs. exploratory study;study designs for learning and confirming;model-free means must pay the price of higher n;definitions of phases;modeling concentration first;model example;phase 2B "cannot be planned in one stroke with all important studies executed in parallel";"learning is intrisically sequential";learning can result in cost savings;list of what one wants to learn from drug development

  • A note concerning a selection “paradox” of Dawid's

    Item Type Journal Article
    Author Stephen Senn
    Date 2008
    Extra Citation Key: sen08not tex.citeulike-article-id= 13265685 tex.posted-at= 2014-07-14 14:10:02 tex.priority= 0
    Volume 62
    Pages 206-210
    Publication Am Statistician
    Issue 3
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • hierarchical-models
    • prior-distributions
    • selection-paradox

    Notes:

    • selecting the treatment with the largest observed mean;"choice of prior is a delicate matter";"... the Bayesian may find it difficult to escape from prior experience when seeking to make a valid inference but find it equally difficult to recognize exactly what that prior experience is.";"... the values of other means in the experiment have no influence. They are simply a random subset of the infiniity of means from which this one has been drawn and with which it is <i>already implicitly being compared</i>."

  • Trying to be precise about vagueness

    Item Type Journal Article
    Author Stephen Senn
    Date 2007
    Extra Citation Key: sen07try tex.citeulike-article-id= 13265565 tex.posted-at= 2014-07-14 14:09:59 tex.priority= 0
    Volume 26
    Pages 1417-1430
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • random-effects
    • meta-analysis
    • bayesian-methods
    • fixed-effects
    • profile-likelihood
    • concise-criticism-of-meta-analysis
    • difficulty-of-choosing-prior-distribution-for-variance-of-random-effects
    • graphical-representation
    • hglm
  • Statistical challenges in functional genomics (with discussion)

    Item Type Journal Article
    Author Paola Sebastiani
    Author Emanuela Gussoni
    Author Isaac S. Kohane
    Author Marco F. Ramoni
    Date 2003
    Extra Citation Key: seb03sta tex.citeulike-article-id= 13265349 tex.posted-at= 2014-07-14 14:09:54 tex.priority= 0
    Volume 18
    Pages 33-70
    Publication Stat Sci
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-methods
    • transformation
    • functional-genomics
    • microarray
    • clustering
    • bioinformatics
    • classification
    • differential-gene-expression

    Notes:

    • excellent overview of genetics, DNA, microarray; other interesting articles follow

  • Interpretation of subgroup analyses in medical device clinical trials

    Item Type Journal Article
    Author Pamela E. Scott
    Author Gregory Campbell
    Date 1998
    Extra Citation Key: sco98int tex.citeulike-article-id= 13264822 tex.posted-at= 2014-07-14 14:09:42 tex.priority= 0
    Volume 32
    Pages 213-220
    Publication Drug Info J
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • teaching
    • subgroup-analysis
    • shrinkage
    • empirical-bayes
    • differential-treatment-effects
  • Estimating the dimension of a model

    Item Type Journal Article
    Author Gideon Schwarz
    Date 1978
    Extra Citation Key: sch78est tex.citeulike-article-id= 13264797 tex.posted-at= 2014-07-14 14:09:42 tex.priority= 0
    Volume 6
    Pages 461-464
    Publication Ann Stat
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • aic
    • bayesian-methods
    • penalty
    • accuracy
    • akaike-information-criterion
  • Bayesian predictive power for interim adaptions in seamless phase II/III trials where the endpoint is survival up to some specified timepoint

    Item Type Journal Article
    Author Heinz Schmidli
    Author Frank Bretz
    Author Amy Racine-Poon
    Date 2007
    Extra Citation Key: sch07bay tex.citeulike-article-id= 13265654 tex.posted-at= 2014-07-14 14:10:01 tex.priority= 0
    Volume 26
    Pages 4925-4938
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • bayesian-methods
    • seamless-phase-ii-iii
  • Robust meta-analytic-predictive priors in clinical trials with historical control information

    Item Type Journal Article
    Author Heinz Schmidli
    Author Sandro Gsteiger
    Author Satrajit Roychoudhury
    Author Anthony O'Hagan
    Author David Spiegelhalter
    Author Beat Neuenschwander
    Date 2014-12
    URL http://dx.doi.org/10.1111/biom.12242
    Extra Citation Key: sch14rob tex.citeulike-article-id= 14287913 tex.citeulike-attachment-1= sch14rob.pdf; /pdf/user/harrelfe/article/14287913/1103337/sch14rob.pdf; 96d13da94bcc08eba74fd3c5ccbc926beba09261 tex.citeulike-linkout-0= http://dx.doi.org/10.1111/biom.12242 tex.posted-at= 2017-02-26 23:07:08 tex.priority= 3
    Volume 70
    Pages 1023-1032
    Publication Biometrics
    DOI 10.1111/biom.12242
    Issue 4
    ISSN 0006341X
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • choice-of-prior
    • historical-data
  • A bootstrap resampling procedure for model building: Application to the Cox regression model

    Item Type Journal Article
    Author Willi Sauerbrei
    Author Martin Schumacher
    Date 1992
    Extra Citation Key: sau92boo tex.citeulike-article-id= 13264795 tex.posted-at= 2014-07-14 14:09:42 tex.priority= 0
    Volume 11
    Pages 2093-2109
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bootstrap
    • variable-selection
    • bayesian-variable-selection
    • post-hoc-power
  • Predictive probability of success using surrogate endpoints

    Item Type Journal Article
    Author Gaelle Saint‐Hilary
    Author Valentine Barboux
    Author Matthieu Pannaux
    Author Mauro Gasparini
    Author Veronique Robert
    Author Gianluca Mastrantonio
    Abstract The predictive probability of success of a future clinical trial is a key quantitative tool for decision-making in drug development. It is derived from prior knowledge and available evidence, and the latter typically comes from the accumulated data on the clinical endpoint of interest in previous clinical trials. However, a surrogate endpoint could be used as primary endpoint in early development and, usually, no or limited data are collected on the clinical endpoint of interest. We propose a general, reliable, and broadly applicable methodology to predict the success of a future trial from surrogate endpoints, in a way that makes the best use of all the available evidence. The predictions are based on an informative prior, called surrogate prior, derived from the results of past trials on one or several surrogate endpoints. If available, in a Bayesian framework, this prior could be combined with data from past trials on the clinical endpoint of interest. Two methods are proposed to address a potential discordance between the surrogate prior and the data on the clinical endpoint. We investigate the patterns of behavior of the predictions in a comprehensive simulation study, and we present an application to the development of a drug in Multiple Sclerosis. The proposed methodology is expected to support decision-making in many different situations, since the use of predictive markers is important to accelerate drug developments and to select promising drug candidates, better and earlier.
    Date 2019
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8060
    Accessed 4/3/2019, 9:04:33 AM
    Rights © 2018 John Wiley & Sons, Ltd.
    Volume 38
    Pages 1753-1774
    Publication Statistics in Medicine
    DOI 10.1002/sim.8060
    Issue 10
    ISSN 1097-0258
    Date Added 4/3/2019, 9:04:33 AM
    Modified 4/3/2019, 9:05:21 AM

    Tags:

    • bayes
    • rct
    • surrogate-endpoint
    • surrogate
    • surrogate-endpoint-criteria
  • Multiple imputation in health-care data bases: An overview and some applications

    Item Type Journal Article
    Author D. Rubin
    Author N. Schenker
    Date 1991
    Extra Citation Key: rub91mul tex.citeulike-article-id= 13264777 tex.posted-at= 2014-07-14 14:09:42 tex.priority= 0
    Volume 10
    Pages 585-598
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • multiple-imputation
    • missing-data
    • approximate-bayesian-bootstrap
    • outcomes-research
  • The Bayesian bootstrap

    Item Type Journal Article
    Author Donald B. Rubin
    Date 1981
    Extra Citation Key: rub81bay tex.citeulike-article-id= 13264775 tex.posted-at= 2014-07-14 14:09:42 tex.priority= 0
    Volume 9
    Pages 130-134
    Publication Appl Stat
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bootstrap
    • bayesian-inference

    Notes:

    • points to Efron showing bootstrap distribution of sample proportions

  • Application of Bayesian approaches in drug development: starting a virtuous cycle

    Item Type Journal Article
    Author Stephen J. Ruberg
    Author Francois Beckers
    Author Rob Hemmings
    Author Peter Honig
    Author Telba Irony
    Author Lisa LaVange
    Author Grazyna Lieberman
    Author James Mayne
    Author Richard Moscicki
    Abstract The pharmaceutical industry and its global regulators have routinely used frequentist statistical methods, such as null hypothesis significance testing and p values, for evaluation and approval of new treatments. The clinical drug development process, however, with its accumulation of data over time, can be well suited for the use of Bayesian statistical approaches that explicitly incorporate existing data into clinical trial design, analysis and decision-making. Such approaches, if used appropriately, have the potential to substantially reduce the time and cost of bringing innovative medicines to patients, as well as to reduce the exposure of patients in clinical trials to ineffective or unsafe treatment regimens. Nevertheless, despite advances in Bayesian methodology, the availability of the necessary computational power and growing amounts of relevant existing data that could be used, Bayesian methods remain underused in the clinical development and regulatory review of new therapies. Here, we highlight the value of Bayesian methods in drug development, discuss barriers to their application and recommend approaches to address them. Our aim is to engage stakeholders in the process of considering when the use of existing data is appropriate and how Bayesian methods can be implemented more routinely as an effective tool for doing so.
    Date 2023-02-15
    Language en
    Short Title Application of Bayesian approaches in drug development
    Library Catalog www.nature.com
    URL https://www.nature.com/articles/s41573-023-00638-0
    Accessed 2/16/2023, 2:20:27 PM
    Rights 2023 Springer Nature Limited
    Extra Publisher: Nature Publishing Group
    Pages 1-16
    Publication Nature Reviews Drug Discovery
    DOI 10.1038/s41573-023-00638-0
    Journal Abbr Nat Rev Drug Discov
    ISSN 1474-1784
    Date Added 2/16/2023, 2:20:27 PM
    Modified 2/16/2023, 2:21:02 PM

    Tags:

    • bayes
    • teaching
    • teaching-mds
    • drug-development
  • Détente: A Practical Understanding of P-values and Bayesian Posterior Probabilities

    Item Type Journal Article
    Author Stephen J. Ruberg
    Abstract Null hypothesis significance testing (NHST) with its benchmark p-value<0.05 has long been a stalwart of scientific reporting and such statistically significant findings have been used to imply scientifically or clinically significant findings. Challenges to this approach have arisen over the past six decades, but they have largely been unheeded. There is a growing movement for using Bayesian statistical inference to quantify the probability that a scientific finding is credible. There have been differences of opinion between the frequentist (i.e. NHST) and Bayesian schools of inference, and warnings about the use or misuse of p-values have come from both schools of thought spanning many decades. Controversies in this arena have been heightened by the American Statistical Association statement on p-values and the further denouncement of the term “statistical significance” by others. My experience has been that many scientists, including many statisticians, do not have a sound conceptual grasp of the fundamental differences in these approaches, thereby creating even greater confusion and acrimony. If we let A represent the observed data, and B represent the hypothesis of interest, then the fundamental distinction between these two approaches can be described as the frequentist approach using the conditional probability pr(A|B), i.e. the p-value, and the Bayesian approach using pr(B|A), the posterior probability. This article will further explain the fundamental differences in NHST and Bayesian approaches and demonstrate how they can co-exist harmoniously to guide clinical trial design and inference.
    Date 2020
    Language en
    Short Title Détente
    Library Catalog Wiley Online Library
    URL https://ascpt.onlinelibrary.wiley.com/doi/abs/10.1002/cpt.2004
    Accessed 8/6/2020, 8:31:38 AM
    Rights This article is protected by copyright. All rights reserved.
    Extra _eprint: https://ascpt.onlinelibrary.wiley.com/doi/pdf/10.1002/cpt.2004
    Volume n/a
    Publication Clinical Pharmacology & Therapeutics
    DOI 10.1002/cpt.2004
    Issue n/a
    ISSN 1532-6535
    Date Added 8/6/2020, 8:31:39 AM
    Modified 8/6/2020, 8:32:23 AM

    Tags:

    • bayes
    • teaching-mds
    • p-value
  • A Bayesian group sequential design for a multiple arm randomized clinical trial

    Item Type Journal Article
    Author G. L. Rosner
    Author D. A. Berry
    Date 1995
    URL http://dx.doi.org/10.1002/sim.4780140405
    Extra Citation Key: ros95bay tex.citeulike-article-id= 13264760 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.4780140405 tex.posted-at= 2014-07-14 14:09:41 tex.priority= 0
    Volume 14
    Pages 381-394
    Publication Stat Med
    DOI 10.1002/sim.4780140405
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • bayesian-inference
    • study-design
    • clinical-trials
    • sequential-monitoring
  • Bayesian transition models for ordinal longitudinal outcomes

    Item Type Journal Article
    Author Maximilian D. Rohde
    Author Benjamin French
    Author Thomas G. Stewart
    Author Frank E. Harrell
    Abstract Ordinal longitudinal outcomes are becoming common in clinical research, particularly in the context of COVID‐19 clinical trials. These outcomes are information‐rich and can increase the statistical efficiency of a study when analyzed in a principled manner. We present Bayesian ordinal transition models as a flexible modeling framework to analyze ordinal longitudinal outcomes. We develop the theory from first principles and provide an application using data from the Adaptive COVID‐19 Treatment Trial (ACTT‐1) with code examples in R. We advocate that researchers use ordinal transition models to analyze ordinal longitudinal outcomes when appropriate alongside standard methods such as time‐to‐event modeling.
    Date 2024-08-15
    Language en
    Library Catalog DOI.org (Crossref)
    URL https://onlinelibrary.wiley.com/doi/10.1002/sim.10133
    Accessed 8/24/2024, 2:46:07 PM
    Volume 43
    Pages 3539-3561
    Publication Statistics in Medicine
    DOI 10.1002/sim.10133
    Issue 18
    Journal Abbr Statistics in Medicine
    ISSN 0277-6715, 1097-0258
    Date Added 8/24/2024, 2:46:07 PM
    Modified 8/24/2024, 2:48:20 PM

    Tags:

    • longitudinal
    • ordinal
    • bayes
    • tutorial
    • transition-model
    • markov
  • Reporting Bayesian Results

    Item Type Journal Article
    Author David Rindskopf
    Abstract Because of the different philosophy of Bayesian statistics, where parameters are random variables and data are considered fixed, the analysis and presentation of results will differ from that of frequentist statistics. Most importantly, the probabilities that a parameter is in certain regions of the parameter space are crucial quantities in Bayesian statistics that are not calculable (or considered important) in the frequentist approach that is the basis of much of traditional statistics. In this article, I discuss the implications of these differences for presentation of the results of Bayesian analyses. In doing so, I present more detailed guidelines than are usually provided and explain the rationale for my suggestions.
    Date December 30, 2020
    Language en
    Library Catalog SAGE Journals
    URL https://doi.org/10.1177/0193841X20977619
    Accessed 1/5/2021, 9:15:07 AM
    Extra Publisher: SAGE Publications Inc
    Pages 0193841X20977619
    Publication Evaluation Review
    DOI 10.1177/0193841X20977619
    Journal Abbr Eval Rev
    ISSN 0193-841X
    Date Added 1/5/2021, 9:15:07 AM
    Modified 1/5/2021, 9:15:56 AM

    Tags:

    • bayes
    • rct
    • teaching-mds
    • reporting
    • reporting-statistical-results
    • reporting-clinical-trials
    • reporting-guidelines
  • A conservative approach to leveraging external evidence for effective clinical trial design

    Item Type Journal Article
    Author Fabio Rigat
    Abstract Abstract Prior probabilities of clinical hypotheses are not systematically used for clinical trial design yet, due to a concern that poor priors may lead to poor decisions. To address this concern, a conservative approach to Bayesian trial design is illustrated here, requiring that the operational characteristics of the primary trial outcome are stronger than the prior. This approach is complementary to current Bayesian design methods, in that it insures against prior‐data conflict by defining a sample size commensurate to a discrete design prior. This approach is ethical, in that it requires designs appropriate to achieving pre‐specified levels of clinical equipoise imbalance. Practical examples are discussed, illustrating design of trials with binary or time to event endpoints. Moderate increases in phase II study sample size are shown to deliver strong levels of overall evidence for go/no‐go clinical development decisions. Levels of negative evidence provided by group sequential confirmatory designs are found negligible, highlighting the importance of complementing efficacy boundaries with non‐binding futility criteria.
    Date 2023-09-26
    Language en
    Library Catalog DOI.org (Crossref)
    URL https://onlinelibrary.wiley.com/doi/10.1002/pst.2339
    Accessed 12/9/2023, 11:18:48 AM
    Pages pst.2339
    Publication Pharmaceutical Statistics
    DOI 10.1002/pst.2339
    Journal Abbr Pharmaceutical Statistics
    ISSN 1539-1604, 1539-1612
    Date Added 12/9/2023, 11:18:48 AM
    Modified 12/9/2023, 11:19:27 AM

    Tags:

    • bayes
    • rct
    • prior
    • prior-elicitation

    Notes:

    • Includes some sample size considerations to ensure that the prior is not too impactful

  • Improving adaptive seamless designs through Bayesian optimization

    Item Type Journal Article
    Author Jakob Richter
    Author Tim Friede
    Author Jörg Rahnenführer
    Abstract We propose to use Bayesian optimization (BO) to improve the efficiency of the design selection process in clinical trials. BO is a method to optimize expensive black-box functions, by using a regression as a surrogate to guide the search. In clinical trials, planning test procedures and sample sizes is a crucial task. A common goal is to maximize the test power, given a set of treatments, corresponding effect sizes, and a total number of samples. From a wide range of possible designs, we aim to select the best one in a short time to allow quick decisions. The standard approach to simulate the power for each single design can become too time consuming. When the number of possible designs becomes very large, either large computational resources are required or an exhaustive exploration of all possible designs takes too long. Here, we propose to use BO to quickly find a clinical trial design with high power from a large number of candidate designs. We demonstrate the effectiveness of our approach by optimizing the power of adaptive seamless designs for different sets of treatment effect sizes. Comparing BO with an exhaustive evaluation of all candidate designs shows that BO finds competitive designs in a fraction of the time.
    Date 2021
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.202000389
    Accessed 2/28/2022, 11:57:27 AM
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202000389
    Volume n/a
    Publication Biometrical Journal
    DOI 10.1002/bimj.202000389
    Issue n/a
    ISSN 1521-4036
    Date Added 2/28/2022, 11:57:27 AM
    Modified 2/28/2022, 11:58:36 AM

    Tags:

    • bayes
    • design
    • optimality
    • adaptive-design
    • adaptive-clinical-trials
    • optimal-design
    • seamless-designs
  • Interleukin-6 Receptor Antagonists in Critically Ill Patients with Covid-19

    Item Type Journal Article
    Author REMAP-CAP Investigators
    Date 2021
    URL https://dx.doi.org/10.1056/nejmoa2100433
    Extra Publisher: Massachusetts Medical Society
    Publication New England Journal of Medicine
    DOI 10.1056/nejmoa2100433
    Journal Abbr New England Journal of Medicine
    ISSN 0028-4793
    Date Added 2/27/2021, 7:28:40 AM
    Modified 2/27/2021, 7:30:16 AM

    Tags:

    • ordinal
    • bayes
    • teaching-mds
    • reporting
    • reporting-clinical-trials
    • adaptive
    • adaptive-clinical-trials
  • A multiple-imputation analysis of a case-control study of the risk of primary cardiact arrest among pharmacologically treated hypertensives

    Item Type Journal Article
    Author Trivellore E. Raghunathan
    Author David S. Siscovick
    Date 1996
    Extra Citation Key: rag96mul tex.citeulike-article-id= 13264723 tex.posted-at= 2014-07-14 14:09:40 tex.priority= 0
    Volume 45
    Pages 335-352
    Publication Appl Stat
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-imputation
    • non-ignorable-missing-data-mechanism
  • Approximate Bayes factors and accounting for model uncertainty in generalised linear models

    Item Type Journal Article
    Author A. E. Raftery
    Date 1996
    Extra Citation Key: raf96app tex.citeulike-article-id= 13264721 tex.posted-at= 2014-07-14 14:09:40 tex.priority= 0
    Volume 83
    Pages 251-266
    Publication Biometrika
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • aic
    • bic
    • variable-selection
    • bayes-factor
    • model-selection
  • Bayesian Methods in Practice: Experiences in the Pharmaceutical Industry

    Item Type Journal Article
    Author A. Racine
    Author A. P. Grieve
    Author H. Fluhler
    Author A. F. M. Smith
    Abstract Four typical applications of Bayesian methods in pharmaceutical research are outlined. The implications of the use of such methods are discussed, and comparisons with traditional methodologies are given.
    Date 1986
    Short Title Bayesian Methods in Practice
    Library Catalog JSTOR
    URL https://www.jstor.org/stable/2347264
    Accessed 11/10/2022, 7:15:11 AM
    Extra Publisher: [Wiley, Royal Statistical Society]
    Volume 35
    Pages 93-150
    Publication Journal of the Royal Statistical Society. Series C (Applied Statistics)
    DOI 10.2307/2347264
    Issue 2
    ISSN 0035-9254
    Date Added 11/10/2022, 7:15:11 AM
    Modified 11/10/2022, 7:15:36 AM

    Tags:

    • bayes
    • rct
    • prior
    • pharmaceutical
  • Bayesian analysis of binary data from an audit of cervical smears

    Item Type Journal Article
    Author Gillian M. Raab
    Author Robert A. Elton
    Date 1993
    Extra Citation Key: raa93bay tex.citeulike-article-id= 13264720 tex.posted-at= 2014-07-14 14:09:40 tex.priority= 0
    Volume 12
    Pages 2179-2189
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • shrinkage
    • bayesian
    • binary-data
  • A Weibull model for survival data: Using prediction to decide when to stop a clinical trial

    Item Type Book Section
    Author J. Qian
    Author D. K. Stangl
    Author S. L. George
    Date 1996
    Extra Citation Key: qia96wei tex.citeulike-article-id= 13264714 tex.posted-at= 2014-07-14 14:09:40 tex.priority= 0
    Place New York
    Publisher Marcel Dekker
    Pages 187-215
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • bayesian-inference
    • parametric-survival-model
    • posterior
    • sequential
    • stopping
    • updating
  • A fully Bayesian approach to calculating sample sizes for clinical trials with binary reponses

    Item Type Journal Article
    Author Hamid Pezeshk
    Author John Gittins
    Date 2002
    Extra Citation Key: pez02ful tex.citeulike-article-id= 13265273 tex.posted-at= 2014-07-14 14:09:53 tex.priority= 0
    Volume 36
    Pages 143-150
    Publication Drug Info J
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • sample-size
    • binomial
    • bayesian-method
    • expected-net-benefit
    • regulator-as-a-third-decision-maker
  • Approximate models for aggregate data when individual-level data sets are very large or unavailable

    Item Type Journal Article
    Author Erol A. Peköz
    Author Michael Shwartz
    Author Cindy L. Christiansen
    Author Dan Berlowitz
    Date 2010
    Extra Citation Key: pek10app tex.citeulike-article-id= 13265845 tex.posted-at= 2014-07-14 14:10:05 tex.priority= 0
    Volume 29
    Pages 2180-2193
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • approximate-bayesian-methods
    • approximating-derivatives-of-log-likelihood-function
    • confidential-data
    • poisson-binomial
  • Joint modeling of recurrent events and survival: a Bayesian non-parametric approach

    Item Type Journal Article
    Author Giorgio Paulon
    Author Maria De Iorio
    Author Alessandra Guglielmi
    Author Francesca Ieva
    Abstract Heart failure (HF) is one of the main causes of morbidity, hospitalization, and death in the western world, and the economic burden associated with HF management is relevant and expected to increase in the future. We consider hospitalization data for HF in the most populated Italian Region, Lombardia. Data were extracted from the administrative data warehouse of the regional healthcare system. The main clinical outcome of interest is time to death and research focus is on investigating how recurrent hospitalizations affect the time to event. The main contribution of the article is to develop a joint model for gap times between consecutive rehospitalizations and survival time. The probability models for the gap times and for the survival outcome share a common patient specific frailty term. Using a flexible Dirichlet process model for %Bayesian nonparametric prior as the random-effects distribution accounts for patient heterogeneity in recurrent event trajectories. Moreover, the joint model allows for dependent censoring of gap times by death or administrative reasons and for the correlations between different gap times for the same individual. It is straightforward to include covariates in the survival and/or recurrence process through the specification of appropriate regression terms. The main advantages of the proposed methodology are wide applicability, ease of interpretation, and efficient computations. Posterior inference is implemented through Markov chain Monte Carlo methods.
    Date 2018
    Language en
    Short Title Joint modeling of recurrent events and survival
    Library Catalog academic.oup.com
    URL https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxy026/5050476
    Accessed 7/8/2018, 7:46:22 AM
    Publication Biostatistics
    DOI 10.1093/biostatistics/kxy026
    Journal Abbr Biostatistics
    Date Added 7/8/2018, 8:12:45 AM
    Modified 7/8/2018, 8:25:31 AM

    Tags:

    • bayes
    • multiple-endpoints
    • rct
    • recurrent-event-with-competing-risk
    • recurrent-events
  • A two-sample test with interval censored data via multiple imputation

    Item Type Journal Article
    Author Wei Pan
    Date 2000
    Extra Citation Key: pan00two tex.citeulike-article-id= 13265092 tex.posted-at= 2014-07-14 14:09:49 tex.priority= 0
    Volume 19
    Pages 1-11
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • simulation-setup
    • approximate-bayesian-bootstrap
  • Flexible random-effects models using Bayesian semi-parametric models: Applications to institutional comparisons

    Item Type Journal Article
    Author Ohlssen
    Author L. D. Sharples
    Author D. J. Spiegelhalter
    Date 2007
    Extra Citation Key: ohl07fle tex.citeulike-article-id= 13265568 tex.posted-at= 2014-07-14 14:09:59 tex.priority= 0
    Volume 26
    Pages 2088-2112
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • scorecarding
    • health-services-research
    • hsr
    • outcomes-research
    • bayesian-nonparametrics
    • dirichlet-process
    • great-winbugs-code-examples
    • institutional-comparisons
    • league-tables
  • Mixed-model imputation of cost data for early discontinuers from a randomized clinical trial

    Item Type Journal Article
    Author Robert L. Obenchain
    Author Bryan M. Johnstone
    Date 1999
    Extra Citation Key: obe99mix tex.citeulike-article-id= 13264636 tex.posted-at= 2014-07-14 14:09:39 tex.priority= 0
    Volume 33
    Pages 191-209
    Publication Drug Info J
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bootstrap
    • analysis-of-cost
    • multiple-imputation
    • shrinkage
    • mixed-model
    • censoring
    • approximate-bayesian-bootstrap
    • cost-per-unit-time
    • smearing-estimator
  • Two cheers for Bayes (letter)

    Item Type Journal Article
    Author Keith O'Rourke
    Date 1996
    Extra Citation Key: oro96two tex.citeulike-article-id= 13264647 tex.posted-at= 2014-07-14 14:09:39 tex.priority= 0
    Volume 17
    Pages 350-351
    Publication Controlled Clin Trials
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • basis-for-inference
  • Bayesian cost-effectiveness analysis from clinical trial data

    Item Type Journal Article
    Author Anthony O'Hagan
    Author John W. Stevens
    Author Jacques Montmartin
    Date 2001
    URL http://dx.doi.org/10.1002/sim.861
    Extra Citation Key: oha01bay tex.citeulike-article-id= 13265187 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.861 tex.posted-at= 2014-07-14 14:09:51 tex.priority= 0
    Volume 20
    Pages 733-753
    Publication Stat Med
    DOI 10.1002/sim.861
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • cost-effectiveness
    • bayesian-model
    • c-e
    • winbugs

    Notes:

    • winbugs example for getting probability of dominance

  • Dicing with the unknown

    Item Type Journal Article
    Author Tony O'Hagan
    Abstract There are many things that I am uncertain about, says Tony O'Hagan. Some are merely unknown to me, while others are unknowable. This article is about different kinds of uncertainty, and how the distinction between them impinges on the foundations of Probability and Statistics.
    Date 2004
    Language en
    Library Catalog Wiley Online Library
    URL https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.1740-9713.2004.00050.x
    Accessed 11/22/2020, 3:49:11 PM
    Rights © 2004 The Royal Statistical Society
    Extra _eprint: https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1740-9713.2004.00050.x
    Volume 1
    Pages 132-133
    Publication Significance
    DOI https://doi.org/10.1111/j.1740-9713.2004.00050.x
    Issue 3
    ISSN 1740-9713
    Date Added 11/22/2020, 3:49:11 PM
    Modified 11/22/2020, 3:49:53 PM

    Tags:

    • bayes
    • teaching
    • teaching-mds
    • probability
  • Bayesian projection approaches to variable selection in generalized linear models

    Item Type Journal Article
    Author David J. Nott
    Author Chenlei Leng
    Abstract A Bayesian approach to variable selection which is based on the expected Kullback–Leibler divergence between the full model and its projection onto a submodel has recently been suggested in the literature. For generalized linear models an extension of this idea is proposed by considering projections onto subspaces defined via some form of L1L1 constraint on the parameter in the full model. This leads to Bayesian model selection approaches related to the lasso. In the posterior distribution of the projection there is positive probability that some components are exactly zero and the posterior distribution on the model space induced by the projection allows exploration of model uncertainty. Use of the approach in structured variable selection problems such as ANOVA models is also considered, where it is desired to incorporate main effects in the presence of interactions. Projections related to the non-negative garotte are able to respect the hierarchical constraints. A consistency result is given concerning the posterior distribution on the model induced by the projection, showing that for some projections related to the adaptive lasso and non-negative garotte the posterior distribution concentrates on the true model asymptotically.
    Date 2010-12
    URL http://dx.doi.org/10.1016/j.csda.2010.01.036
    Extra Citation Key: not10bay tex.citeulike-article-id= 6660033 tex.citeulike-attachment-1= not10bay.pdf; /pdf/user/harrelfe/article/6660033/1072316/not10bay.pdf; f8fd0acb578c766ba3479da9adb036de784ecbe2 tex.citeulike-linkout-0= http://dx.doi.org/10.1016/j.csda.2010.01.036 tex.day= 10 tex.posted-at= 2016-06-14 01:01:11 tex.priority= 2
    Volume 54
    Pages 3227-3241
    Publication Computational Statistics & Data Analysis
    DOI 10.1016/j.csda.2010.01.036
    Issue 12
    ISSN 01679473
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • bayesian-methods
    • variable-selection
    • model-approximation
    • pre-conditioning
  • Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data: Application to Insomnia Data

    Item Type Journal Article
    Author Noorian, Sajad
    Author Ganjali, Mojtaba
    Abstract Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data: Application to Insomnia Data
    Date 2012
    Language en
    Short Title Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data
    Library Catalog www.academia.edu
    URL https://www.academia.edu/30796618/Bayesian_Analysis_of_Transition_Model_for_Longitudinal_Ordinal_Response_Data_Application_to_Insomnia_Data
    Accessed 12/22/2020, 8:21:14 AM
    Volume 1
    Pages 148-161
    Publication International Journal of Statistics in Medical Research
    Issue 2
    ISSN 1929-6029
    Date Added 12/22/2020, 8:21:14 AM
    Modified 1/24/2021, 7:21:47 AM

    Tags:

    • ordinal
    • bayes
    • serial
    • markov

    Notes:

    • Bayesian inference for Goodman-Kruskal gamma rank correlation using multinomial distribution and Dirichlet prior.  Markov proportional odds model with priors for intercepts that are ordered t distribution variates.  Also uses a sequential-conditioning Markov-like prior for the coefficients of previous states.  Methods don't scale to high number of Y levels.  Dataset used is not a very good one as it categorized an ordinal measurement into a very crude ordinal measurement.  Transition model is categorical in previous Y level.

  • Approximate Bayesian inference with the weighted likelihood bootstrap (with discussion)

    Item Type Journal Article
    Author Michael A. Newton
    Author Adrian E. Rafter
    Date 1994
    Extra Citation Key: new94app tex.citeulike-article-id= 13264622 tex.posted-at= 2014-07-14 14:09:39 tex.priority= 0
    Volume 56
    Pages 3-48
    Publication J Roy Stat Soc B
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bootstrap
    • bayesian-inference
    • weighted-mle
  • Predictively consistent prior effective sample sizes

    Item Type Journal Article
    Author Beat Neuenschwander
    Author Sebastian Weber
    Author Heinz Schmidli
    Author Anthony O'Hagan
    Abstract Determining the sample size of an experiment can be challenging, even more so when incorporating external information via a prior distribution. Such information is increasingly used to reduce the size of the control group in randomized clinical trials. Knowing the amount of prior information, expressed as an equivalent prior effective sample size (ESS), clearly facilitates trial designs. Various methods to obtain a prior's ESS have been proposed recently. They have been justified by the fact that they give the standard ESS for one-parameter exponential families. However, despite being based on similar information-based metrics, they may lead to surprisingly different ESS for non-conjugate settings, which complicates many designs with prior information. We show that current methods fail a basic predictive consistency criterion, which requires the expected posterior–predictive ESS for a sample of size N to be the sum of the prior ESS and N. The expected local-information-ratio ESS is introduced and shown to be predictively consistent. It corrects the ESS of current methods, as shown for normally distributed data with a heavy-tailed Student-t prior and exponential data with a generalized Gamma prior. Finally, two applications are discussed: the prior ESS for the control group derived from historical data, and the posterior ESS for hierarchical subgroup analyses. This article is protected by copyright. All rights reserved
    Date 2020
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13252
    Accessed 3/7/2020, 6:21:14 AM
    Rights This article is protected by copyright. All rights reserved
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13252
    Volume n/a
    Publication Biometrics
    DOI 10.1111/biom.13252
    Issue n/a
    ISSN 1541-0420
    Date Added 3/7/2020, 6:21:14 AM
    Modified 3/7/2020, 6:21:57 AM

    Tags:

    • bayes
    • prior
    • effective-sample-size
  • The current state of Bayesian methods in medical product development: survey results and recommendations from the DIA Bayesian Scientific Working Group

    Item Type Journal Article
    Author Fanni Natanegara
    Author Beat Neuenschwander
    Author John W. Seaman
    Author Nelson Kinnersley
    Author Cory R. Heilmann
    Author David Ohlssen
    Author George Rochester
    Date 2014-01
    URL http://dx.doi.org/10.1002/pst.1595
    Extra Citation Key: nat14cur tex.citeulike-article-id= 14530069 tex.citeulike-attachment-1= nat14cur.pdf; /pdf/user/harrelfe/article/14530069/1128781/nat14cur.pdf; 75e2b6ad8c3ad854b5fbf1dcb2371a50199da228 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/pst.1595 tex.posted-at= 2018-02-06 03:05:05 tex.priority= 0
    Volume 13
    Pages 3-12
    Publication Pharm Stat
    DOI 10.1002/pst.1595
    Issue 1
    ISSN 15391604
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • drug-development
    • regulatory-viewpoint
  • Domperidone Increases Harmful Cardiac Events in Parkinson's Disease: A Bayesian Re-Analysis of an Observational Study

    Item Type Journal Article
    Author Gisèle Nakhlé
    Author James M. Brophy
    Author Christel Renoux
    Author Paul Khairy
    Author Patrick Bélisle
    Author Jacques LeLorier
    Abstract Over the last two decades, Bayesian methods have gained popularity and their implementations have widened in statistical sciences and applied fields. From a healthcare perspective, regulatory agencies are now accepting Bayesian approaches for earlier phases of drug development [1,2] and comparative effectiveness research [3,4]. At the drug development level, Bayesian adaptive analytical approaches have been particularly attractive for achieving greater efficiency in reducing sample size, time and cost of trials [5].
    Date 2021/09/07
    Language English
    Short Title Domperidone Increases Harmful Cardiac Events in Parkinson's Disease
    Library Catalog www.jclinepi.com
    URL https://www.jclinepi.com/article/S0895-4356(21)00282-1/abstract
    Accessed 9/8/2021, 11:34:40 AM
    Extra Publisher: Elsevier
    Volume 0
    Publication Journal of Clinical Epidemiology
    DOI 10.1016/j.jclinepi.2021.09.002
    Issue 0
    Journal Abbr Journal of Clinical Epidemiology
    ISSN 0895-4356, 1878-5921
    Date Added 9/8/2021, 11:34:40 AM
    Modified 9/8/2021, 11:35:12 AM

    Tags:

    • bayes
    • observational-data
    • observational-study
  • A Phase 2 Randomized Placebo-Controlled Adjuvant Trial of GI-4000, a Recombinant Yeast Expressing Mutated RAS Proteins in Patients with Resected Pancreas Cancer

    Item Type Journal Article
    Author Peter Muscarella
    Author Tanios Bekaii-Saab
    Author Kristi McIntyre
    Author Alexander Rosemurgy
    Author Sharona B. Ross
    Author Donald A. Richards
    Author William E. Fisher
    Author Patrick J. Flynn
    Author Alicia Mattson
    Author Claire Coeshott
    Author Heinrich Roder
    Author Joanna Roder
    Author Frank E. Harrell
    Author Allen Cohn
    Author Timothy C. Rodell
    Author David Apelian
    Abstract Purpose: GI-4000, a series of recombinant yeast expressing four different mutated RAS proteins, was evaluated in subjects with resected ras-mutated pancreas cancer.Methods: Subjects (n = 176) received GI-4000 or placebo plus gemcitabine. Subjects' tumors were genotyped to identify which matched GI-4000 product to administer. Immune responses were measured by interferon-γ (IFNγ) ELISpot assay and by regulatory T cell (Treg) frequencies on treatment. Pretreatment plasma was retrospectively analyzed by matrix-assisted laser desorption/ionization-time-of-flight (MALDI-ToF) mass spectrometry for proteomic signatures predictive of GI-4000 responsiveness.Results: GI-4000 was well tolerated, with comparable safety findings between treatment groups. The GI-4000 group showed a similar pattern of median recurrence-free and overall survival (OS) compared with placebo. For the prospectively defined and stratified R1 resection subgroup, there was a trend in 1 year OS (72% vs. 56%), an improvement in OS (523.5 vs. 443.5 days [hazard ratio (HR) = 1.06 [confidence interval (CI): 0.53–2.13], p = 0.872), and increased frequency of immune responders (40% vs. 8%; p = 0.062) for GI-4000 versus placebo and a 159-day improvement in OS for R1 GI-4000 immune responders versus placebo (p = 0.810). For R0 resection subjects, no increases in IFNγ responses in GI-4000–treated subjects were observed. A higher frequency of R0/R1 subjects with a reduction in Tregs (CD4+/CD45RA+/Foxp3low) was observed in GI-4000–treated subjects versus placebo (p = 0.033). A proteomic signature was identified that predicted response to GI-4000/gemcitabine regardless of resection status.Conclusion: These results justify continued investigation of GI-4000 in studies stratified for likely responders or in combination with immune check-point inhibitors or other immunomodulators, which may provide optimal reactivation of antitumor immunity.ClinicalTrials.gov Number: NCT00300950.
    Date March 1, 2021
    Library Catalog liebertpub.com (Atypon)
    URL https://www.liebertpub.com/doi/10.1089/pancan.2020.0021
    Accessed 3/30/2021, 7:26:13 AM
    Extra Publisher: Mary Ann Liebert, Inc., publishers
    Volume 7
    Pages 8-19
    Publication Journal of Pancreatic Cancer
    DOI 10.1089/pancan.2020.0021
    Issue 1
    Date Added 3/30/2021, 7:26:13 AM
    Modified 4/4/2021, 8:21:16 AM

    Tags:

    • bayes
    • rct
    • oncology-rct
    • sequential
    • not-recent
  • Extending the family of Bayesian bootstraps and exchangeable urn schemes

    Item Type Journal Article
    Author Pietro Muliere
    Author Stephen Walker
    Date 1998
    Extra Citation Key: mul98ext tex.citeulike-article-id= 13264611 tex.posted-at= 2014-07-14 14:09:38 tex.priority= 0
    Volume 60
    Pages 175-182
    Publication J Roy Stat Soc B
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-bootstrap
    • bootstrapping-censored-data
    • finite-population
    • urn
  • Bayesian variable selection in linear regression

    Item Type Journal Article
    Author T. J. Mitchell
    Author J. J. Beauchamp
    Date 1988
    Extra Citation Key: mit88bay tex.citeulike-article-id= 13264600 tex.posted-at= 2014-07-14 14:09:38 tex.priority= 0
    Volume 83
    Pages 1023-1036
    Publication J Am Stat Assoc
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-methods
    • variable-selection
    • model-selection
  • Bayesian Model Choice in Cumulative Link Ordinal Regression Models

    Item Type Journal Article
    Author Trevelyan J. McKinley
    Author Michelle Morters
    Author James L. N. Wood
    Abstract The use of the proportional odds (PO) model for ordinal regression is ubiquitous in the literature. If the assumption of parallel lines does not hold for the data, then an alternative is to specify a non-proportional odds (NPO) model, where the regression parameters are allowed to vary depending on the level of the response. However, it is often difficult to fit these models, and challenges regarding model choice and fitting are further compounded if there are a large number of explanatory variables. We make two contributions towards tackling these issues: firstly, we develop a Bayesian method for fitting these models, that ensures the stochastic ordering conditions hold for an arbitrary finite range of the explanatory variables, allowing NPO models to be fitted to any observed data set. Secondly, we use reversible-jump Markov chain Monte Carlo to allow the model to choose between PO and NPO structures for each explanatory variable, and show how variable selection can be incorporated. These methods can be adapted for any monotonic increasing link functions. We illustrate the utility of these approaches on novel data from a longitudinal study of individual-level risk factors affecting body condition score in a dog population in Zenzele, South Africa.
    Date 2015-03
    Language EN
    Library Catalog Project Euclid
    URL https://projecteuclid.org/euclid.ba/1422468421
    Accessed 1/19/2019, 8:07:15 AM
    Extra MR: MR3420895 Zbl: 1334.62141
    Volume 10
    Pages 1-30
    Publication Bayesian Analysis
    DOI 10.1214/14-BA884
    Issue 1
    Journal Abbr Bayesian Anal.
    ISSN 1936-0975, 1931-6690
    Date Added 1/19/2019, 8:07:15 AM
    Modified 1/19/2019, 8:07:57 AM

    Tags:

    • ordinal
    • bayes
    • proportional-odds
    • partial-proportional-odds
  • Statistical rethinking : a Bayesian course with examples in R and Stan

    Item Type Book
    Author Richard McElreath
    Date 2016
    URL http://www.worldcat.org/isbn/9781482253443
    Extra Citation Key: mce16sta tex.citeulike-article-id= 14255283 tex.citeulike-linkout-0= http://www.worldcat.org/isbn/9781482253443 tex.citeulike-linkout-1= http://books.google.com/books?vid=ISBN9781482253443 tex.citeulike-linkout-2= http://www.amazon.com/gp/search?keywords=9781482253443&index=books&linkCode=qs tex.citeulike-linkout-3= http://www.librarything.com/isbn/9781482253443 tex.citeulike-linkout-4= http://www.worldcat.org/oclc/920672225 tex.posted-at= 2017-01-15 19:24:57 tex.priority= 4
    ISBN 978-1-4822-5344-3
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • teaching-mds
    • bayesian-methods
  • Bayesian propensity score analysis for observational data

    Item Type Journal Article
    Author Lawrence C. McCandless
    Author Paul Gustafson
    Author Peter C. Austin
    Date 2009
    Extra Citation Key: mcc09bay tex.citeulike-article-id= 13265723 tex.posted-at= 2014-07-14 14:10:02 tex.priority= 0
    Volume 28
    Pages 94-112
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • confounding
    • causal-inference
    • propensity-score
    • observational-study
    • bayesian-statistics
    • bias

    Notes:

    • using Bayesian credible intervals to adjust for uncertainty in estimation of propensity score;relied heavily on Rubin 5-category propensity adjustment

  • A Bayesian hierarchical survival model for the institutional effects in a multi-centre cancer clinical trial

    Item Type Journal Article
    Author Yutaka Matsuyama
    Author Junichi Sakamoto
    Author Yasuo Ohashi
    Date 1998
    URL http://dx.doi.org/10.1002/(SICI)1097-0258(19980915)17:17%3C1893::AID-SIM878%3E3.3.CO;2-I
    Extra Citation Key: mat98bay tex.citeulike-article-id= 13264585 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/(SICI)1097-0258(19980915)17:17%3C1893::AID-SIM878%3E3.3.CO;2-I tex.posted-at= 2014-07-14 14:09:38 tex.priority= 0
    Volume 17
    Pages 1893-1908
    Publication Stat Med
    DOI 10.1002/(SICI)1097-0258(19980915)17:17\%3C1893::AID-SIM878\%3E3.3.CO;2-I
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • bayesian-inference
    • multi-center-trial
    • hierarchical-model
    • site-by-treatment-interaction
  • Statistical issues in the prospective monitoring of health outcomes across multiple units

    Item Type Journal Article
    Author Clare Marshall
    Author Nicky Best
    Author Alex Bottle
    Author Paul Aylin
    Abstract Summary. Following several recent inquiries in the UK into medical malpractice and failures to deliver appropriate standards of health care, there is pressure to introduce formal monitoring of performance outcomes routinely throughout the National Health Service. Statistical process control (SPC) charts have been widely used to monitor medical outcomes in a variety of contexts and have been specifically advocated for use in clinical governance. However, previous applications of SPC charts in medical monitoring have focused on surveillance of a single process over time. We consider some of the methodological and practical aspects that surround the routine surveillance of health outcomes and, in particular, we focus on two important methodological issues that arise when attempting to extend SPC charts to monitor outcomes at more than one unit simultaneously (where a unit could be, for example, a surgeon, general practitioner or hospital): the need to acknowledge the inevitable between-unit variation in ‘acceptable’ performance outcomes due to the net effect of many small unmeasured sources of variation (e.g. unmeasured case mix and data errors) and the problem of multiple testing over units as well as time. We address the former by using quasi-likelihood estimates of overdispersion, and the latter by using recently developed methods based on estimation of false discovery rates. We present an application of our approach to annual monitoring ‘all-cause’ mortality data between 1995 and 2000 from 169 National Health Service hospital trusts in England and Wales.
    Date 2004
    Language en
    Library Catalog Wiley Online Library
    URL https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-985X.2004.apm10.x
    Accessed 7/8/2020, 5:55:40 AM
    Extra _eprint: https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-985X.2004.apm10.x
    Volume 167
    Pages 541-559
    Publication Journal of the Royal Statistical Society: Series A (Statistics in Society)
    DOI 10.1111/j.1467-985X.2004.apm10.x
    Issue 3
    ISSN 1467-985X
    Date Added 7/8/2020, 5:55:40 AM
    Modified 7/8/2020, 5:57:02 AM

    Tags:

    • bayes
    • quality-assurance
    • provider-profiling
    • scorecard
    • scorecarding
    • outcomes-research
  • Contrasting Bayesian analysis of survey data and clinical trials

    Item Type Journal Article
    Author Donald J. Malec
    Date 2001
    Extra Citation Key: mal01con tex.citeulike-article-id= 13265192 tex.posted-at= 2014-07-14 14:09:51 tex.priority= 0
    Volume 20
    Pages 1363-1371
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • choice-of-prior
    • bayesian-methods
    • contrast-with-frequentist
    • model-checking
    • nonparametric-evaluation-of-models
    • selection-bias
    • sensitivity-analysis
  • Bayes offers a `New' way to make sense of numbers

    Item Type Journal Article
    Author David Malakoff
    Date 1999
    URL http://dx.doi.org/10.1126/science.286.5444.1460
    Extra Citation Key: mal99bay tex.citeulike-article-id= 13265096 tex.citeulike-linkout-0= http://dx.doi.org/10.1126/science.286.5444.1460 tex.posted-at= 2014-07-14 14:09:49 tex.priority= 0
    Volume 286
    Pages 1460-1464
    Publication Science
    DOI 10.1126/science.286.5444.1460
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • teaching
    • clinical-trials
  • Seamlessly expanding a randomized phase II trial to phase III

    Item Type Journal Article
    Author Y. T. Lurdes
    Author P. F. Thall
    Author D. A. Berry
    Date 2002
    URL http://dx.doi.org/10.1111/j.0006-341X.2002.00823.x
    Extra Citation Key: lur02sea tex.citeulike-article-id= 13265653 tex.citeulike-linkout-0= http://dx.doi.org/10.1111/j.0006-341X.2002.00823.x tex.posted-at= 2014-07-14 14:10:01 tex.priority= 0
    Volume 58
    Pages 823-831
    Publication Biometrics
    DOI 10.1111/j.0006-341X.2002.00823.x
    Issue 4
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • bayesian-methods
    • drug-development
    • seamless-phase-ii-iii
  • The BUGS project: Evolution, critique and future directions (with discussion)

    Item Type Journal Article
    Author David Lunn
    Author David Spiegelhalter
    Author Andrew Thomas
    Author Nicky Best
    Date 2009
    Extra Citation Key: lun09bug tex.citeulike-article-id= 13265785 tex.posted-at= 2014-07-14 14:10:04 tex.priority= 0
    Volume 28
    Pages 3049-3082
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bugs
    • bayesian-modeling
    • openbugs
  • Cumulative logit models for ordinal data: a case study involving allergic rhinitis severity scores

    Item Type Journal Article
    Author David J. Lunn
    Author Jon Wakefield
    Author Amy Racine-Poon
    Abstract Ordered categorical data arise in numerous settings, a common example being pain scores in analgesic trials. The modelling of such data is intrinsically more difficult than the modelling of continuous data due to the constraints on the underlying probabilities and the reduced amount of information that discrete outcomes contain. In this paper we discuss the class of cumulative logit models, which provide a natural framework for ordinal data analysis. We show how viewing the categorical outcome as the discretization of an underlying continuous response allows a natural interpretation of model parameters. We also show how covariates are incorporated into the model and how various types of correlation among repeated measures on the same individual may be accounted for. The models are illustrated using longitudinal allergy data consisting of sneezing scores measured on a four-point scale. Our approach throughout is Bayesian and we present a range of simple diagnostics to aid model building. Copyright © 2001 John Wiley & Sons, Ltd.
    Date 2001
    Language en
    Short Title Cumulative logit models for ordinal data
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.922
    Accessed 10/21/2021, 9:08:27 AM
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.922
    Volume 20
    Pages 2261-2285
    Publication Statistics in Medicine
    DOI 10.1002/sim.922
    Issue 15
    ISSN 1097-0258
    Date Added 10/21/2021, 9:08:27 AM
    Modified 10/21/2021, 9:11:10 AM

    Tags:

    • ordinal
    • bayes
    • random-effects
    • serial
    • markov

    Notes:

    • Has an example where variance of random effects is greatly reduced when modeling serial dependence using a Markov model vs. using the ordinary random effects model, stating that within-subject variation is mostly explained by serial correlation.

  • Evaluating Causes of Effects by Posterior Effects of Causes

    Item Type Journal Article
    Author Zitong Lu
    Author Zhi Geng
    Author Wei Li
    Author Shengyu Zhu
    Author Jinzhu Jia
    Abstract For the case with a single causal variable, Dawid et al. (2014) defined the probability of causation and Pearl (2000) defined the probability of necessity to assess the causes of effects. For a case with multiple causes which may affect each other, this paper defines the posterior total and direct causal effects based on the evidences observed for post-treatment variables, which could be viewed as measurements of causes of effects. Posterior causal effects involve the probabilities of counterfactual variables. Thus, like probability of causation, probability of necessity and the direct causal effects, the identifiability of posterior total and direct causal effects requires more assumptions than the identifiability of traditional causal effects conditional on pre-treatment variables. We present assumptions required for the identifiability of posterior causal effects and provide identification equations. Further, when the causal relationships among multiple causes and an endpoint may be depicted by causal networks, we can simplify both the required assumptions and the identification equations of the posterior total and direct causal effects. Finally, using numerical examples, we compare the posterior total and direct causal effects with other measures for evaluating the causes of effects and the population attributable risks.
    Date 2022-07-09
    Library Catalog Silverchair
    URL https://doi.org/10.1093/biomet/asac038
    Accessed 8/18/2022, 7:01:14 AM
    Pages asac038
    Publication Biometrika
    DOI 10.1093/biomet/asac038
    Journal Abbr Biometrika
    ISSN 1464-3510
    Date Added 8/18/2022, 7:01:44 AM
    Modified 8/18/2022, 7:02:47 AM

    Tags:

    • bayes
    • causality
    • causal-effects
  • Bayesian approaches to variable selection: a comparative study from practical perspectives

    Item Type Journal Article
    Author Zihang Lu
    Author Wendy Lou
    Abstract In many clinical studies, researchers are interested in parsimonious models that simultaneously achieve consistent variable selection and optimal prediction. The resulting parsimonious models will facilitate meaningful biological interpretation and scientific findings. Variable selection via Bayesian inference has been receiving significant advancement in recent years. Despite its increasing popularity, there is limited practical guidance for implementing these Bayesian approaches and evaluating their comparative performance in clinical datasets. In this paper, we review several commonly used Bayesian approaches to variable selection, with emphasis on application and implementation through R software. These approaches can be roughly categorized into four classes: namely the Bayesian model selection, spike-and-slab priors, shrinkage priors, and the hybrid of both. To evaluate their variable selection performance under various scenarios, we compare these four classes of approaches using real and simulated datasets. These results provide practical guidance to researchers who are interested in applying Bayesian approaches for the purpose of variable selection.
    Date 2021-03-24
    Language en
    Short Title Bayesian approaches to variable selection
    Library Catalog www.degruyter.com
    URL https://www.degruyter.com/document/doi/10.1515/ijb-2020-0130/html?_llca=transfer%3A6c4e2be67680df8697a36347096dc061&_llch=4a014e430be2a0321edc05c36ca125ee393e28b730c6fd6acf796c4547770148
    Accessed 1/26/2022, 9:49:34 AM
    Extra Publisher: De Gruyter
    Publication The International Journal of Biostatistics
    DOI 10.1515/ijb-2020-0130
    ISSN 1557-4679
    Date Added 1/26/2022, 9:49:34 AM
    Modified 1/26/2022, 9:50:04 AM

    Tags:

    • bayes
    • variable-selection
  • Variational Bayes Estimation of Discrete-Margined Copula Models With Application to Time Series

    Item Type Journal Article
    Author Rubén Loaiza-Maya
    Author Michael Stanley Smith
    Abstract We propose a new variational Bayes (VB) estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins. The method is based on a variational approximation to a tractable augmented posterior and is faster than previous likelihood-based approaches. We use it to estimate drawable vine copulas for univariate and multivariate Markov ordinal and mixed time series. These have dimension rT, where T is the number of observations and r is the number of series, and are difficult to estimate using previous methods. The vine pair-copulas are carefully selected to allow for heteroscedasticity, which is a feature of most ordinal time series data. When combined with flexible margins, the resulting time series models also allow for other common features of ordinal data, such as zero inflation, multiple modes, and under or overdispersion. Using six example series, we illustrate both the flexibility of the time series copula models and the efficacy of the VB estimator for copulas of up to 792 dimensions and 60 parameters. This far exceeds the size and complexity of copula models for discrete data that can be estimated using previous methods. An online appendix and MATLAB code implementing the method are available as supplementary materials.
    Date January 14, 2019
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/10618600.2018.1562936
    Accessed 4/10/2019, 9:13:07 AM
    Volume 0
    Pages 1-17
    Publication Journal of Computational and Graphical Statistics
    DOI 10.1080/10618600.2018.1562936
    Issue 0
    ISSN 1061-8600
    Date Added 4/10/2019, 9:13:07 AM
    Modified 4/10/2019, 9:13:37 AM

    Tags:

    • bayes
    • copula
  • Epistemic uncertainty in Bayesian predictive probabilities

    Item Type Journal Article
    Author Charles C. Liu
    Author Ron Xiaolong Yu
    Abstract Bayesian predictive probabilities have become a ubiquitous tool for design and monitoring of clinical trials. The typical procedure is to average predictive probabilities over the prior or posterior distributions. In this paper, we highlight the limitations of relying solely on averaging, and propose the reporting of intervals or quantiles for the predictive probabilities. These intervals formalize the intuition that uncertainty decreases with more information. We present four different applications (Phase 1 dose escalation, early stopping for futility, sample size re-estimation, and assurance/probability of success) to demonstrate the practicality and generality of the proposed approach.
    Date 2023-05-08
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/10543406.2023.2204943
    Accessed 5/10/2023, 8:28:07 AM
    Extra Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10543406.2023.2204943 PMID: 37157818
    Volume 0
    Pages 1-19
    Publication Journal of Biopharmaceutical Statistics
    DOI 10.1080/10543406.2023.2204943
    Issue 0
    ISSN 1054-3406
    Date Added 5/10/2023, 8:28:07 AM
    Modified 5/10/2023, 8:28:36 AM

    Tags:

    • bayes
    • rct
    • monitoring
    • predictive-distribution
  • A Bayesian Time-Varying Coefficient Model for Multitype Recurrent Events

    Item Type Journal Article
    Author Yi Liu
    Author Feng Guo
    Abstract This article proposes a Bayesian time-varying coefficient model to evaluate the temporal profile of intensity for multitype recurrent events. The model obtains smooth estimates for both time-varying coefficients and the baseline intensity using Bayesian penalized splines. One major challenge in Bayesian penalized splines is that the smoothness of a spline fit is sensitive to the subjective choice of hyperparameters. We establish a procedure to objectively determine the hyperparameters through robust prior specification. To effectively update the high-dimensional spline parameters, we develop a Markov chain Monte Carlo procedure based on the Metropolis-adjusted Langevin algorithms. A joint sampling scheme is used to achieve better convergence and mixing properties. A simulation study confirms satisfactory model performance in estimating time-varying coefficients under different curvature and event rate scenarios. Application to a commercial truck driver naturalistic driving data reveals that drivers with 7-hours-or-less sleep time have a significantly higher safety-critical event intensity after 8 hr of driving and the intensity remains high after taking a break. The findings provide crucial information for the truck driver hours-of-service regulation and fatigue management. The proposed model provides a flexible and robust tool to evaluate the temporal profile of intensity for multitype recurrent events. Supplemental materials for this article are available online.
    Date October 31, 2019
    Library Catalog amstat.tandfonline.com (Atypon)
    URL https://amstat.tandfonline.com/doi/abs/10.1080/10618600.2019.1686988
    Accessed 12/16/2019, 7:59:14 AM
    Pages 1-12
    Publication Journal of Computational and Graphical Statistics
    DOI 10.1080/10618600.2019.1686988
    Journal Abbr Journal of Computational and Graphical Statistics
    ISSN 1061-8600
    Date Added 12/16/2019, 7:59:14 AM
    Modified 12/16/2019, 7:59:42 AM

    Tags:

    • bayes
    • recurrent-events
    • time-varying-coefficients
  • A semiparametric method of multiple imputation

    Item Type Journal Article
    Author Stuart R. Lipsitz
    Author Lue P. Zhao
    Author Geert Molenberghs
    Date 1998
    Extra Citation Key: lip98sem tex.citeulike-article-id= 13264534 tex.posted-at= 2014-07-14 14:09:37 tex.priority= 0
    Volume 60
    Pages 127-144
    Publication J Roy Stat Soc B
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • multiple-imputation
    • missing-data
    • bayesian-bootstrap
  • Bayesian nonparametric analysis of longitudinal studies in the presence of informative missingness

    Item Type Journal Article
    Author A. R. Linero
    Date 2017-06
    URL http://dx.doi.org/10.1093/biomet/asx015
    Extra Citation Key: lin17bay tex.citeulike-article-id= 14360238 tex.citeulike-linkout-0= http://dx.doi.org/10.1093/biomet/asx015 tex.posted-at= 2017-05-19 22:09:38 tex.priority= 2
    Volume 104
    Pages 327-341
    Publication Biometrika
    DOI 10.1093/biomet/asx015
    Issue 2
    ISSN 0006-3444
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • serial-data
    • missing-data
    • informative-dropout
    • longitudinal-data
    • bayesian-modeling
  • The Analysis of Experimental Data: The Appreciation of Tea and Wine

    Item Type Journal Article
    Author Dennis V. Lindley
    Abstract A classical experiment on the tasting of tea is used to show that many standard methods of analysis of the resulting data are unsatisfactory. A similar experiment with wine is used to show how a more sensible method may be developed.
    Date 1993-03
    URL http://dx.doi.org/10.1111/j.1467-9639.1993.tb00252.x
    Extra Citation Key: lin93ana tex.citeulike-article-id= 10418027 tex.citeulike-attachment-1= lin93ana.pdf; /pdf/user/harrelfe/article/10418027/1121742/lin93ana.pdf; 243d4fbea879999e1f76b707d0e2502d5aca542f tex.citeulike-linkout-0= http://dx.doi.org/10.1111/j.1467-9639.1993.tb00252.x tex.day= 1 tex.posted-at= 2017-10-31 12:04:19 tex.priority= 0 tex.publisher= Blackwell Publishing Ltd
    Volume 15
    Pages 22-25
    Publication Teaching Statistics
    DOI 10.1111/j.1467-9639.1993.tb00252.x
    Issue 1
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • teaching-mds
    • teaching-statisticians
  • Ensuring exchangeability in data-based priors for a Bayesian analysis of clinical trials

    Item Type Journal Article
    Author Junjing Lin
    Author Margaret Gamalo-Siebers
    Author Ram Tiwari
    Abstract In many orphan diseases and pediatric indications, the randomized controlled trials may be infeasible because of their size, duration, and cost. Leveraging information on the control through a prior can potentially reduce sample size. However, unless an objective prior is used to impose complete ignorance for the parameter being estimated, it results in biased estimates and inflated type-I error. Hence, it is essential to assess both the confirmatory and supplementary knowledge available during the construction of the prior to avoid “cherry-picking” advantageous information. For this purpose, propensity score methods are employed to minimize selection bias by weighting supplemental control subjects according to their similarity in terms of pretreatment characteristics to the subjects in the current trial. The latter can be operationalized through a proposed measure of overlap in propensity-score distributions. In this paper, we consider single experimental arm in the current trial and the control arm is completely borrowed from the supplemental data. The simulation experiments show that the proposed method reduces prior and data conflict and improves the precision of the of the average treatment effect.
    Date 2021
    Language en
    Library Catalog Wiley Online Library
    URL http://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2172
    Accessed 9/30/2021, 11:15:42 AM
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2172
    Volume n/a
    Publication Pharmaceutical Statistics
    DOI 10.1002/pst.2172
    Issue n/a
    ISSN 1539-1612
    Date Added 9/30/2021, 11:15:42 AM
    Modified 9/30/2021, 11:16:11 AM

    Tags:

    • bayes
    • prior
    • exchangeability
  • The statistical basis of public policy: A paradigm shift is overdue

    Item Type Journal Article
    Author R. J. Lilford
    Author D. Braunholtz
    Date 1996
    Extra Citation Key: lil96sta tex.citeulike-article-id= 13264515 tex.posted-at= 2014-07-14 14:09:37 tex.priority= 0
    Volume 313
    Pages 603-607
    Publication BMJ
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • teaching-mds
    • excellent-for-teaching-bayesian-methods-and-explaining-the-advantages
    • adjusting-for-study-bias-or-quality
  • Analyzing ordinal data with metric models: What could possibly go wrong?

    Item Type Journal Article
    Author Torrin M. Liddell
    Author John K. Kruschke
    Abstract We surveyed all articles in the Journal of Personality and Social Psychology (JPSP), Psychological Science (PS), and the Journal of Experimental Psychology: General (JEP:G) that mentioned the term “Likert,” and found that 100% of the articles that analyzed ordinal data did so using a metric model. We present novel evidence that analyzing ordinal data as if they were metric can systematically lead to errors. We demonstrate false alarms (i.e., detecting an effect where none exists, Type I errors) and failures to detect effects (i.e., loss of power, Type II errors). We demonstrate systematic inversions of effects, for which treating ordinal data as metric indicates the opposite ordering of means than the true ordering of means. We show the same problems — false alarms, misses, and inversions — for interactions in factorial designs and for trend analyses in regression. We demonstrate that averaging across multiple ordinal measurements does not solve or even ameliorate these problems. A central contribution is a graphical explanation of how and when the misrepresentations occur. Moreover, we point out that there is no sure-fire way to detect these problems by treating the ordinal values as metric, and instead we advocate use of ordered-probit models (or similar) because they will better describe the data. Finally, although frequentist approaches to some ordered-probit models are available, we use Bayesian methods because of their flexibility in specifying models and their richness and accuracy in providing parameter estimates. An R script is provided for running an analysis that compares ordered-probit and metric models.
    Date November 1, 2018
    Short Title Analyzing ordinal data with metric models
    Library Catalog ScienceDirect
    URL http://www.sciencedirect.com/science/article/pii/S0022103117307746
    Accessed 1/7/2019, 1:55:52 PM
    Volume 79
    Pages 328-348
    Publication Journal of Experimental Social Psychology
    DOI 10.1016/j.jesp.2018.08.009
    Journal Abbr Journal of Experimental Social Psychology
    ISSN 0022-1031
    Date Added 1/7/2019, 1:55:52 PM
    Modified 1/7/2019, 1:56:25 PM

    Tags:

    • ordinal
    • bayes
    • robustness
  • A Bayesian approach for individual-level drug benefit-risk assessment

    Item Type Journal Article
    Author Kan Li
    Author Sheng Luo
    Author Sammy Yuan
    Author Shahrul Mt‐Isa
    Abstract In existing benefit-risk assessment (BRA) methods, benefit and risk criteria are usually identified and defined separately based on aggregated clinical data and therefore ignore the individual-level differences as well as the association among the criteria. We proposed a Bayesian multicriteria decision-making method for BRA of drugs using individual-level data. We used a multidimensional latent trait model to account for the heterogeneity of treatment effects with latent variables introducing the dependencies among outcomes. We then applied the stochastic multicriteria acceptability analysis approach for BRA incorporating imprecise and heterogeneous patient preference information. We adopted an efficient Markov chain Monte Carlo algorithm when implementing the proposed method. We applied our method to a case study to illustrate how individual-level benefit-risk profiles could inform decision-making.
    Date 2019
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8166
    Accessed 4/16/2019, 9:06:38 AM
    Rights © 2019 John Wiley & Sons, Ltd.
    Volume 0
    Publication Statistics in Medicine
    DOI 10.1002/sim.8166
    Issue 0
    ISSN 1097-0258
    Date Added 4/16/2019, 9:06:38 AM
    Modified 4/16/2019, 9:09:18 AM

    Tags:

    • bayes
    • multiple-endpoints
    • rct
    • decision-theory
    • drug-development
    • risk-benefit-ratio
    • latent-variable
    • multicriteria-decision
  • Regression modelling of diagnostic likelihood ratios for the evaluation of medical diagnostic tests

    Item Type Journal Article
    Author Wendy Leisenring
    Author Margaret S. Pepe
    Date 1998
    Extra Citation Key: lei98reg tex.citeulike-article-id= 13264499 tex.posted-at= 2014-07-14 14:09:36 tex.priority= 0
    Volume 54
    Pages 444-452
    Publication Biometrics
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • gee
    • bayes-factor
    • clustered-data
    • sensitivity
    • specificity
    • predictive-value
    • audiology
    • diagnosis-and-testing
    • likelihood-ratios
  • Implementing the Bayesian paradigm: reporting research results over the World-Wide Web.

    Item Type Journal Article
    Author H. P. Lehmann
    Author M. R. Wachter
    Abstract For decades, statisticians, philosophers, medical investigators and others interested in data analysis have argued that the Bayesian paradigm is the proper approach for reporting the results of scientific analyses for use by clients and readers. To date, the methods have been too complicated for non-statisticians to use. In this paper we argue that the World-Wide Web provides the perfect environment to put the Bayesian paradigm into practice: the likelihood function of the data is parsimoniously represented on the server side, the reader uses the client to represent her prior belief, and a downloaded program (a Java applet) performs the combination. In our approach, a different applet can be used for each likelihood function, prior belief can be assessed graphically, and calculation results can be reported in a variety of ways. We present a prototype implementation, BayesApplet, for two-arm clinical trials with normally-distributed outcomes, a prominent model for clinical trials. The primary implication of this work is that publishing medical research results on the Web can take a form beyond or different from that currently used on paper, and can have a profound impact on the publication and use of research results.
    Date 1996
    URL http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2232964/
    Extra Citation Key: leh96imp tex.citeulike-article-id= 13346740 tex.citeulike-attachment-1= leh96imp.pdf; /pdf/user/harrelfe/article/13346740/983544/leh96imp.pdf; b5a59f8e18230cb4ddc17759b426db8f88cb2e69 tex.citeulike-linkout-0= http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2232964/ tex.citeulike-linkout-1= http://view.ncbi.nlm.nih.gov/pubmed/8947703 tex.citeulike-linkout-2= http://www.hubmed.org/display.cgi?uids=8947703 tex.pmcid= PMC2232964 tex.pmid= 8947703 tex.posted-at= 2014-09-04 12:57:17 tex.priority= 0
    Pages 433-437
    Publication Proceedings : a conference of the American Medical Informatics Association / ... AMIA Annual Fall Symposium. AMIA Fall Symposium
    ISSN 1091-8280
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • teaching-mds
  • Bayesian communication of research results over the World Wide Web

    Item Type Journal Article
    Author Harold P. Lehmann
    Author Bach Nguyen
    Date 1997
    URL http://www.ncbi.nlm.nih.gov/pubmed/9308343
    Extra Citation Key: leh97bay tex.citeulike-article-id= 13264497 tex.citeulike-linkout-0= http://www.ncbi.nlm.nih.gov/pubmed/9308343 tex.posted-at= 2014-07-14 14:09:36 tex.priority= 0
    Volume 14
    Pages 353-359
    Publication M.D. Computing
    Issue 5
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-methods
    • varying-prior-using-www
    • web-based-teaching
  • Bayesian clinical trials in action

    Item Type Journal Article
    Author Jack J. Lee
    Author Caleb T. Chu
    Abstract Although the frequentist paradigm has been the predominant approach to clinical trial design since the 1940s, it has several notable limitations. Advancements in computational algorithms and computer hardware have greatly enhanced the alternative Bayesian paradigm. Compared with its frequentist counterpart, the Bayesian framework has several unique advantages, and its incorporation into clinical trial design is occurring more frequently. Using an extensive literature review to assess how Bayesian methods are used in clinical trials, we find them most commonly used for dose finding, efficacy monitoring, toxicity monitoring, diagnosis/decision making, and studying pharmacokinetics/pharmacodynamics. The additional infrastructure required for implementing Bayesian methods in clinical trials may include specialized software programs to run the study design, simulation and analysis, and web-based applications, all of which are particularly useful for timely data entry and analysis. Trial success requires not only the development of proper tools but also timely and accurate execution of data entry, quality control, adaptive randomization, and Bayesian computation. The relative merit of the Bayesian and frequentist approaches continues to be the subject of debate in statistics. However, more evidence can be found showing the convergence of the two camps, at least at the practical level. Ultimately, better clinical trial methods lead to more efficient designs, lower sample sizes, more accurate conclusions, and better outcomes for patients enrolled in the trials. Bayesian methods offer attractive alternatives for better trials. More Bayesian trials should be designed and conducted to refine the approach and demonstrate their real benefit in action.
    Date 2012
    URL http://dx.doi.org/10.1002/sim.5404
    Extra Citation Key: lee12bay tex.citeulike-article-id= 13265978 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.5404 tex.posted-at= 2014-07-14 14:10:08 tex.priority= 0
    Volume 31
    Pages 2955-2972
    Publication Stat Med
    DOI 10.1002/sim.5404
    Issue 25
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • adaptive-trial-design
    • bayesian-paradigm
    • clinical-trial-conduct
    • frequentist-paradigm
    • trial-efficiency
    • trial-ethics
  • Bayesian adaptive determination of the sample size required to assure acceptably low adverse event risk

    Item Type Journal Article
    Author A. Lawrence Gould
    Author Xiaohua D. Zhang
    Abstract An emerging concern with new therapeutic agents, especially treatments for type 2 diabetes, a prevalent condition that increases an individual's risk of heart attack or stroke, is the likelihood of adverse events, especially cardiovascular events, that the new agents may cause. These concerns have led to regulatory requirements for demonstrating that a new agent increases the risk of an adverse event relative to a control by no more than, say, 30% or 80% with high (e.g., 97.5%) confidence. We describe a Bayesian adaptive procedure for determining if the sample size for a development program needs to be increased and, if necessary, by how much, to provide the required assurance of limited risk. The decision is based on the predictive likelihood of a sufficiently high posterior probability that the relative risk is no more than a specified bound. Allowance can be made for between-center as well as within-center variability to accommodate large-scale developmental programs, and design alternatives (e.g., many small centers, few large centers) for obtaining additional data if needed can be explored. Binomial or Poisson likelihoods can be used, and center-level covariates can be accommodated. The predictive likelihoods are explored under various conditions to assess the statistical properties of the method.
    Date 2014-03
    URL http://dx.doi.org/10.1002/sim.5993
    Extra Citation Key: gou14bay tex.citeulike-article-id= 13448164 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.5993 tex.day= 15 tex.posted-at= 2014-11-29 16:36:41 tex.priority= 2
    Volume 33
    Pages 940-957
    Publication Stat Med
    DOI 10.1002/sim.5993
    Issue 6
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • sample-size
    • pharmaceutical-safety
    • adaptive-design
    • adverse-events
    • clinical-safety
  • Predictive model selection

    Item Type Journal Article
    Author Purushottam W. Laud
    Author Joseph G. Ibrahim
    Date 1995
    Extra Citation Key: lau95pre tex.citeulike-article-id= 13264465 tex.posted-at= 2014-07-14 14:09:36 tex.priority= 0
    Volume 57
    Pages 247-262
    Publication J Roy Stat Soc B
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • aic
    • bic
    • variable-selection
    • bayes-factor
    • model-selection
    • maximum-likelihood
    • transformation-selection
  • Grouped random effects models for Bayesian meta-analysis

    Item Type Journal Article
    Author Daniel T. Larose
    Author Dipak K. Dey
    Date 1997
    Extra Citation Key: lar97gro tex.citeulike-article-id= 13264463 tex.posted-at= 2014-07-14 14:09:36 tex.priority= 0
    Volume 16
    Pages 1817-1829
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • meta-analysis
    • random-effects-model
    • grouping-random-effects-by-study-type
  • Effect of Therapeutic Hypothermia Initiated After 6 Hours of Age on Death or Disability Among Newborns With Hypoxic-Ischemic Encephalopathy

    Item Type Journal Article
    Author Abbot R. Laptook
    Author Seetha Shankaran
    Author Jon E. Tyson
    Author Breda Munoz
    Author Edward F. Bell
    Author Ronald N. Goldberg
    Author Nehal A. Parikh
    Author Namasivayam Ambalavanan
    Author Claudia Pedroza
    Author Athina Pappas
    Author Abhik Das
    Author Aasma S. Chaudhary
    Author Richard A. Ehrenkranz
    Author Angelita M. Hensman
    Author Krisa P. Van Meurs
    Author Lina F. Chalak
    Author Shannon E. G. Hamrick
    Author Gregory M. Sokol
    Author Michele C. Walsh
    Author Brenda B. Poindexter
    Author Roger G. Faix
    Author Kristi L. Watterberg
    Author Ivan D. Frantz
    Author Ronnie Guillet
    Author Uday Devaskar
    Author William E. Truog
    Author Valerie Y. Chock
    Author Myra H. Wyckoff
    Author Elisabeth C. McGowan
    Author David P. Carlton
    Author Heidi M. Harmon
    Author Jane E. Brumbaugh
    Author C. Michael Cotten
    Author Pablo J. Sánchez
    Author Anna M. Hibbs
    Author Rosemary D. Higgins
    Date 2017-10
    URL http://dx.doi.org/10.1001/jama.2017.14972
    Extra Citation Key: lap17eff tex.citeulike-article-id= 14468904 tex.citeulike-attachment-1= lap17eff.pdf; /pdf/user/harrelfe/article/14468904/1121693/lap17eff.pdf; 45c4faa44eaae4bdc24b392bc4934ead9e872b35 tex.citeulike-linkout-0= http://dx.doi.org/10.1001/jama.2017.14972 tex.day= 24 tex.posted-at= 2017-10-30 16:03:37 tex.priority= 0
    Volume 318
    Pages 1550+
    Publication JAMA
    DOI 10.1001/jama.2017.14972
    Issue 16
    ISSN 0098-7484
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • bayesian-inference
  • How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS

    Item Type Journal Article
    Author Paul C. Lambert
    Author Alex J. Sutton
    Author Paul R. Burton
    Author Keith R. Abrams
    Author David R. Jones
    Date 2005
    Extra Citation Key: lam05how tex.citeulike-article-id= 13265432 tex.posted-at= 2014-07-14 14:09:56 tex.priority= 0
    Volume 24
    Pages 2401-2428
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • graphics
    • bayesian-methods
    • mcmc
    • prior-distributions
    • simulation-study

    Notes:

    • uninformative prior distributions showed large effects of results for scale parameters in meta-analysis with small numbers of studies;beautiful graphical summaries of results

  • Discovering structure in multiple outcomes models for tests of childhood neurodevelopment

    Item Type Journal Article
    Author Amy LaLonde
    Author Tanzy Love
    Author Sally W. Thurston
    Author Philip W. Davidson
    Abstract Bayesian model–based clustering provides a powerful and flexible tool that can be incorporated into regression models to better understand the grouping of observations. Using data from the Seychelles Child Development Study, we explore the effect of prenatal methylmercury exposure on 20 neurodevelopmental outcomes measured in 9-year-old children. Rather than cluster individual subjects, we cluster the outcomes within a multiple outcomes model. By using information in the data to nest the outcomes into groups called domains, the model more accurately reflects the shared characteristics of neurodevelopmental domains and improves estimation of the overall and outcome-specific exposure effects by shrinking effects within and between domains selected by the data. The Bayesian paradigm allows for sampling from the posterior distribution of the grouping parameters; thus, inference can be made about group membership and their defining characteristics. We avoid the often difficult and highly subjective requirement of a priori identification of the total number of groups by incorporating a Dirichlet process prior to form a fully Bayesian multiple outcomes model.
    Date 2020
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13174
    Accessed 11/29/2019, 2:24:03 PM
    Rights © 2019 The International Biometric Society
    Volume n/a
    Publication Biometrics
    DOI 10.1111/biom.13174
    Issue n/a
    ISSN 1541-0420
    Date Added 11/29/2019, 2:24:03 PM
    Modified 11/29/2019, 2:24:58 PM

    Tags:

    • bayes
    • multiple-endpoints
    • clustering
  • Bayesian adaptive design for clinical trials in Duchenne muscular dystrophy

    Item Type Journal Article
    Author Stephen L. Lake
    Author Melanie A. Quintana
    Author Kristine Broglio
    Author Jennifer Panagoulias
    Author Scott M. Berry
    Author Michael A. Panzara
    Abstract A Bayesian adaptive design is proposed for a clinical trial in Duchenne muscular dystrophy. The trial was designed to demonstrate treatment efficacy on an ambulatory-based clinical endpoint and to identify early success on a biomarker (dystrophin protein levels) that can serve as a basis for accelerated approval in the United States. The trial incorporates placebo augmentation using placebo data from past clinical trials. A thorough simulation study was conducted to understand the operating characteristics of the trial. This trial design was selected for the US FDA Complex Innovative Trial Design Pilot Meeting Program and the experience in that program is summarized.
    Date 2021
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9021
    Accessed 5/8/2021, 8:36:35 AM
    Rights © 2021 John Wiley & Sons, Ltd.
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9021
    Volume n/a
    Publication Statistics in Medicine
    DOI https://doi.org/10.1002/sim.9021
    Issue n/a
    ISSN 1097-0258
    Date Added 5/8/2021, 8:36:35 AM
    Modified 5/8/2021, 8:37:48 AM

    Tags:

    • bayes
    • rct
    • adaptive
    • biomarker
  • Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned

    Item Type Journal Article
    Author Fabiola La Gamba
    Author Tom Jacobs
    Author Helena Geys
    Author Thomas Jaki
    Author Jan Serroyen
    Author Moreno Ursino
    Author Alberto Russu
    Author Christel Faes
    Abstract The present manuscript aims to discuss the implications of sequential knowledge integration of small preclinical trials in a Bayesian pharmacokinetic and pharmacodynamic (PK-PD) framework. While, at first sight, a Bayesian PK-PD framework seems to be a natural framework to allow for sequential knowledge integration, the scope of this paper is to highlight some often-overlooked challenges while at the same time providing some guidances in the many and overwhelming choices that need to be made. Challenges as well as opportunities will be discussed that are related to the impact of (1) the prior specification, (2) the choice of random effects, (3) the type of sequential integration method. In addition, it will be shown how the success of a sequential integration strategy is highly dependent on a carefully chosen experimental design when small trials are analyzed.
    Date April 1, 2019
    Short Title Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework
    Library Catalog onlinelibrary.wiley.com (Atypon)
    URL https://onlinelibrary.wiley.com/doi/full/10.1002/pst.1941
    Accessed 4/2/2019, 9:26:53 AM
    Volume 0
    Publication Pharmaceutical Statistics
    DOI 10.1002/pst.1941
    Issue 0
    Journal Abbr Pharmaceutical Statistics
    ISSN 1539-1604
    Date Added 4/2/2019, 9:26:54 AM
    Modified 4/2/2019, 9:27:59 AM

    Tags:

    • bayes
    • pharmaceutical
    • drug-development
    • pk
  • A Review of Bayesian Perspectives on Sample Size Derivation for Confirmatory Trials

    Item Type Journal Article
    Author Kevin Kunzmann
    Author Michael J. Grayling
    Author Kim May Lee
    Author David S. Robertson
    Author Kaspar Rufibach
    Author James M. S. Wason
    Abstract Sample size derivation is a crucial element of planning any confirmatory trial. The required sample size is typically derived based on constraints on the maximal acceptable Type I error rate and minimal desired power. Power depends on the unknown true effect and tends to be calculated either for the smallest relevant effect or a likely point alternative. The former might be problematic if the minimal relevant effect is close to the null, thus requiring an excessively large sample size, while the latter is dubious since it does not account for the a priori uncertainty about the likely alternative effect. A Bayesian perspective on sample size derivation for a frequentist trial can reconcile arguments about the relative a priori plausibility of alternative effects with ideas based on the relevance of effect sizes. Many suggestions as to how such “hybrid” approaches could be implemented in practice have been put forward. However, key quantities are often defined in subtly different ways in the literature. Starting from the traditional entirely frequentist approach to sample size derivation, we derive consistent definitions for the most commonly used hybrid quantities and highlight connections, before discussing and demonstrating their use in sample size derivation for clinical trials.
    Date March 22, 2021
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/00031305.2021.1901782
    Accessed 4/23/2021, 10:59:37 AM
    Extra Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00031305.2021.1901782
    Volume 0
    Pages 1-9
    Publication The American Statistician
    DOI 10.1080/00031305.2021.1901782
    Issue 0
    ISSN 0003-1305
    Date Added 4/23/2021, 10:59:37 AM
    Modified 4/23/2021, 11:00:06 AM

    Tags:

    • bayes
    • rct
    • sample-size
    • design
  • Bayesian data analysis for newcomers

    Item Type Journal Article
    Author John K. Kruschke
    Author Torrin M. Liddell
    Date 2017
    URL http://dx.doi.org/10.3758/s13423-017-1272-1
    Extra Citation Key: kru17bay tex.booktitle= Psychonomic Bulletin & Review tex.citeulike-article-id= 14379017 tex.citeulike-attachment-1= kru17bay.pdf; /pdf/user/harrelfe/article/14379017/1112234/kru17bay.pdf; 667a350e04440965997f085062e0249269d20ce3 tex.citeulike-linkout-0= http://dx.doi.org/10.3758/s13423-017-1272-1 tex.citeulike-linkout-1= http://link.springer.com/article/10.3758/s13423-017-1272-1 tex.posted-at= 2017-06-19 02:27:08 tex.priority= 0 tex.publisher= Springer US
    Pages 1-23
    DOI 10.3758/s13423-017-1272-1
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • teaching
    • teaching-mds

    Notes:

    • Excellent for teaching Bayesian methods and explaining the advantages

  • Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan

    Item Type Book
    Author John K. Kruschke
    Date 2015
    URL http://www.sciencedirect.com/science/book/9780124058880
    Extra Citation Key: kru15doi tex.citeulike-article-id= 14172337 tex.citeulike-linkout-0= http://www.sciencedirect.com/science/book/9780124058880 tex.posted-at= 2016-10-26 21:46:24 tex.priority= 4
    Place Waltham MA
    Publisher Academic Press
    ISBN 978-0-12-405888-0
    Edition Second Edition
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • teaching-mds
    • bayesian-methods
  • Bayesian estimation supersedes the t test.

    Item Type Journal Article
    Author John K. Kruschke
    Abstract Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the normality of the data. The method handles outliers. The decision rule can accept the null value (unlike traditional t tests) when certainty in the estimate is high (unlike Bayesian model comparison using Bayes factors). The method also yields precise estimates of statistical power for various research goals. The software and programs are free and run on Macintosh, Windows, and Linux platforms. PsycINFO Database Record (c) 2013 APA, all rights reserved.
    Date 2013-05
    URL http://dx.doi.org/10.1037/a0029146
    Extra Citation Key: kru13bay tex.citeulike-article-id= 11639960 tex.citeulike-attachment-1= kru13bay.pdf; /pdf/user/harrelfe/article/11639960/1136836/kru13bay.pdf; dea60927efbd1f284b4132eae3461ea7ce0fb62a tex.citeulike-linkout-0= http://dx.doi.org/10.1037/a0029146 tex.citeulike-linkout-1= http://view.ncbi.nlm.nih.gov/pubmed/22774788 tex.citeulike-linkout-2= http://www.hubmed.org/display.cgi?uids=22774788 tex.day= 9 tex.pmid= 22774788 tex.posted-at= 2018-05-18 03:54:13 tex.priority= 4
    Volume 142
    Pages 573-603
    Publication J Exp Psych
    DOI 10.1037/a0029146
    Issue 2
    ISSN 1939-2222
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • tutorial
    • teaching-mds
    • basic
  • Power gains by using external information in clinical trials are typically not possible when requiring strict type I error control

    Item Type Journal Article
    Author Annette Kopp‐Schneider
    Author Silvia Calderazzo
    Author Manuel Wiesenfarth
    Abstract In the era of precision medicine, novel designs are developed to deal with flexible clinical trials that incorporate many treatment strategies for multiple diseases in one trial setting. This situation often leads to small sample sizes in disease-treatment combinations and has fostered the discussion about the benefits of borrowing of external or historical information for decision-making in these trials. Several methods have been proposed that dynamically discount the amount of information borrowed from historical data based on the conformity between historical and current data. Specifically, Bayesian methods have been recommended and numerous investigations have been performed to characterize the properties of the various borrowing mechanisms with respect to the gain to be expected in the trials. However, there is common understanding that the risk of type I error inflation exists when information is borrowed and many simulation studies are carried out to quantify this effect. To add transparency to the debate, we show that if prior information is conditioned upon and a uniformly most powerful test exists, strict control of type I error implies that no power gain is possible under any mechanism of incorporation of prior information, including dynamic borrowing. The basis of the argument is to consider the test decision function as a function of the current data even when external information is included. We exemplify this finding in the case of a pediatric arm appended to an adult trial and dichotomous outcome for various methods of dynamic borrowing from adult information to the pediatric arm. In conclusion, if use of relevant external data is desired, the requirement of strict type I error control has to be replaced by more appropriate metrics.
    Date 2019
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.201800395
    Accessed 7/7/2019, 9:29:11 AM
    Rights © 2019 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim
    Volume 0
    Publication Biometrical Journal
    DOI 10.1002/bimj.201800395
    Issue 0
    ISSN 1521-4036
    Date Added 7/7/2019, 9:29:11 AM
    Modified 7/7/2019, 9:30:26 AM

    Tags:

    • bayes
    • prior
    • power
    • historical-data
    • borrow-information
  • A semiparametric Bayesian approach to the random effects model

    Item Type Journal Article
    Author Ken P. Kleinman
    Author Joseph G. Ibrahim
    Date 1998
    Extra Citation Key: ble98sem tex.citeulike-article-id= 13263776 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0
    Volume 54
    Pages 921-938
    Publication Biometrics
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • random-effects
    • dirichlet-prior-for-random-effects
  • A general semiparametric Bayesian discrete-time recurrent events model

    Item Type Journal Article
    Author Adam J. King
    Author Robert E. Weiss
    Abstract SUMMARY. Event time variables are often recorded in a discrete fashion, especially in the case of patient-reported outcomes. This work is motivated by a study
    Date 2019
    Language en
    Library Catalog academic.oup.com
    URL https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxz029/5542991
    Accessed 8/4/2019, 8:01:22 AM
    Publication Biostatistics
    DOI 10.1093/biostatistics/kxz029
    Journal Abbr Biostatistics
    Date Added 8/4/2019, 8:01:22 AM
    Modified 8/4/2019, 8:02:00 AM

    Tags:

    • bayes
    • recurrent-event-with-competing-risk
    • recurrent-events
    • competing-risk
  • Bayesian sensitivity analysis for causal effects from 2 tables in the presence of unmeasured confounding with application to presidential campaign visits

    Item Type Journal Article
    Author Luke Keele
    Author Kevin M. Quinn
    Date 2017-12
    URL https://doi.org/10.1214/17-AOAS1048
    Extra Citation Key: kee17bay tex.citeulike-article-id= 14546623 tex.citeulike-linkout-0= http://dx.doi.org/10.1214/17-AOAS1048 tex.citeulike-linkout-1= https://doi.org/10.1214/17-AOAS1048 tex.posted-at= 2018-03-09 20:04:17 tex.priority= 2
    Volume 11
    Pages 1974-1997
    Publication Ann App Stat
    DOI 10.1214/17-AOAS1048
    Issue 4
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • confounding
    • bayes
    • bayesian-inference
    • causal-inference
    • sensitivity-analysis
  • Beyond “Treatment Versus Control”: How Bayesian Analysis Makes Factorial Experiments Feasible in Education Research

    Item Type Journal Article
    Author Daniel Kassler
    Author Ira Nichols-Barrer
    Author Mariel Finucane
    Abstract Background:Researchers often wish to test a large set of related interventions or approaches to implementation. A factorial experiment accomplishes this by examining not only basic treatment?control comparisons but also the effects of multiple implementation ?factors? such as different dosages or implementation strategies and the interactions between these factor levels. However, traditional methods of statistical inference may require prohibitively large sample sizes to perform complex factorial experiments.Objectives:We present a Bayesian approach to factorial design. Through the use of hierarchical priors and partial pooling, we show how Bayesian analysis substantially increases the precision of estimates in complex experiments with many factors and factor levels, while controlling the risk of false positives from multiple comparisons.Research design:Using an experiment we performed for the U.S. Department of Education as a motivating example, we perform power calculations for both classical and Bayesian methods. We repeatedly simulate factorial experiments with a variety of sample sizes and numbers of treatment arms to estimate the minimum detectable effect (MDE) for each combination.Results:The Bayesian approach yields substantially lower MDEs when compared with classical methods for complex factorial experiments. For example, to test 72 treatment arms (five factors with two or three levels each), a classical experiment requires nearly twice the sample size as a Bayesian experiment to obtain a given MDE.Conclusions:Bayesian methods are a valuable tool for researchers interested in studying complex interventions. They make factorial experiments with many treatment arms vastly more feasible.
    Date January 10, 2019
    Language en
    Short Title Beyond “Treatment Versus Control”
    Library Catalog SAGE Journals
    URL https://doi.org/10.1177/0193841X18818903
    Accessed 8/13/2019, 9:31:24 AM
    Pages 0193841X18818903
    Publication Evaluation Review
    DOI 10.1177/0193841X18818903
    Journal Abbr Eval Rev
    ISSN 0193-841X
    Date Added 8/13/2019, 9:31:24 AM
    Modified 8/13/2019, 9:32:26 AM

    Tags:

    • bayes
    • sample-size
    • interaction
    • hierarchical-model
  • A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion

    Item Type Journal Article
    Author Robert E. Kass
    Author Larry Wasserman
    Date 1995
    Extra Citation Key: kas95ref tex.citeulike-article-id= 13264388 tex.posted-at= 2014-07-14 14:09:34 tex.priority= 0
    Volume 90
    Pages 928-934
    Publication J Am Stat Assoc
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bic
    • variable-selection
    • bayes-factor
    • model-selection
    • schwarz-criterion
  • The selection of prior distributions by formal rules

    Item Type Journal Article
    Author Robert E. Kass
    Author Larry Wasserman
    Date 1996
    Extra Citation Key: kas96sel tex.citeulike-article-id= 13264389 tex.posted-at= 2014-07-14 14:09:34 tex.priority= 0
    Volume 91
    Pages 1343-1370
    Publication J Am Stat Assoc
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • choice-of-prior
    • automatic-priors-using-formal-rules
  • Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-the-Losers Design

    Item Type Journal Article
    Author Alex Karanevich
    Author Richard Meier
    Author Stefan Graw
    Author Anna McGlothlin
    Author Byron Gajewski
    Abstract When a researcher desires to test several treatment arms against a control arm, a two-stage adaptive design can be more efficient than a single-stage design where patients are equally allocated to all treatment arms and the control. We see this type of approach in clinical trials as a seamless Phase II–Phase III design. These designs require more statistical support and are less straightforward to plan and analyze than a standard single-stage design. To diminish the barriers associated with a Bayesian two-stage drop-the-losers design, we built a user-friendly point-and-click graphical user interface with R Shiny to aid researchers in planning such designs by allowing them to easily obtain trial operating characteristics, estimate statistical power and sample size, and optimize patient allocation in each stage to maximize power. We assume that endpoints are distributed normally with unknown but common variance between treatments. We recommend this software as an easy way to engage statisticians and researchers in two-stage designs as well as to actively investigate the power of two-stage designs relative to more traditional approaches. The software is freely available at https://github.com/stefangraw/Allocation-Power-Optimizer.
    Date May 3, 2019
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/00031305.2019.1610065
    Accessed 9/20/2020, 10:37:25 AM
    Extra Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00031305.2019.1610065
    Volume 0
    Pages 1-10
    Publication The American Statistician
    DOI 10.1080/00031305.2019.1610065
    Issue 0
    ISSN 0003-1305
    Date Added 9/20/2020, 10:37:25 AM
    Modified 9/20/2020, 10:39:09 AM

    Tags:

    • bayes
    • sample-size
    • adaptive
  • Randomized Trial of Three Anticonvulsant Medications for Status Epilepticus

    Item Type Journal Article
    Author Jaideep Kapur
    Author Jordan Elm
    Author James M. Chamberlain
    Author William Barsan
    Author James Cloyd
    Author Daniel Lowenstein
    Author Shlomo Shinnar
    Author Robin Conwit
    Author Caitlyn Meinzer
    Author Hannah Cock
    Author Nathan Fountain
    Author Jason T. Connor
    Author Robert Silbergleit
    Date November 28, 2019
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1056/NEJMoa1905795
    Accessed 11/29/2019, 2:26:32 PM
    Volume 381
    Pages 2103-2113
    Publication New England Journal of Medicine
    DOI 10.1056/NEJMoa1905795
    Issue 22
    ISSN 0028-4793
    Date Added 11/29/2019, 2:26:32 PM
    Modified 11/29/2019, 2:27:12 PM

    Tags:

    • bayes
    • rct
  • Bayesian sample-size determination methods considering both worthwhileness and unpromisingness for exploratory two-arm randomized clinical trials with binary endpoints

    Item Type Journal Article
    Author Tomoyuki Kakizume
    Author Fanghong Zhang
    Author Yohei Kawasaki
    Author Takashi Daimon
    Abstract A randomized exploratory clinical trial comparing an experimental treatment with a control treatment on a binary endpoint is often conducted to make a go or no-go decision. Such an exploratory trial needs to have an adequate sample size such that it will provide convincing evidence that the experimental treatment is either worthwhile or unpromising relative to the control treatment. In this paper, we propose three new sample-size determination methods for an exploratory trial, which utilize the posterior probabilities calculated from predefined efficacy and inefficacy criteria leading to a declaration of the worthwhileness or unpromisingness of the experimental treatment. Simulation studies, including numerical investigation, showed that all three methods could declare the experimental treatment as worthwhile or unpromising with a high probability when the true response probability of the experimental treatment group is higher or lower, respectively, than that of the control treatment group.
    Date 2019
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1971
    Accessed 9/15/2019, 5:25:49 PM
    Rights © 2019 John Wiley & Sons, Ltd.
    Volume 0
    Publication Pharmaceutical Statistics
    DOI 10.1002/pst.1971
    Issue 0
    ISSN 1539-1612
    Date Added 9/15/2019, 5:25:49 PM
    Modified 9/15/2019, 5:26:23 PM

    Tags:

    • bayes
    • sample-size
  • Reply to letter by O'Rourke

    Item Type Journal Article
    Author Joseph B. Kadane
    Date 1996
    Extra Citation Key: kad96rep tex.citeulike-article-id= 13264374 tex.posted-at= 2014-07-14 14:09:34 tex.priority= 0
    Volume 17
    Pages 352
    Publication Controlled Clin Trials
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference

    Notes:

    • On posterior being a function of the prior

  • Bayesian sensitivity analyses for longitudinal data with dropouts that are potentially missing not at random: A high dimensional pattern-mixture mode

    Item Type Journal Article
    Author Niko A. Kaciroti
    Author Roderick J. A. Little
    Abstract Randomized clinical trials with outcome measured longitudinally are frequently analyzed using either random effect models or generalized estimating equations. Both approaches assume that the dropout mechanism is missing at random (MAR) or missing completely at random (MCAR). We propose a Bayesian pattern-mixture model to incorporate missingness mechanisms that might be missing not at random (MNAR), where the distribution of the outcome measure at the follow-up time tk, conditional on the prior history, differs across the patterns of missing data. We then perform sensitivity analysis on estimates of the parameters of interest. The sensitivity parameters relate the distribution of the outcome of interest between subjects from a missing-data pattern at time tk with that of the observed subjects at time tk. The large number of the sensitivity parameters is reduced by treating them as random with a prior distribution having some pre-specified mean and variance, which are varied to explore the sensitivity of inferences. The missing at random (MAR) mechanism is a special case of the proposed model, allowing a sensitivity analysis of deviations from MAR. The proposed approach is applied to data from the Trial of Preventing Hypertension.
    Date 2021
    Language en
    Short Title Bayesian sensitivity analyses for longitudinal data with dropouts that are potentially missing not at random
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9083
    Accessed 6/3/2021, 10:26:33 AM
    Rights © 2021 John Wiley & Sons Ltd.
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9083
    Volume n/a
    Publication Statistics in Medicine
    DOI https://doi.org/10.1002/sim.9083
    Issue n/a
    ISSN 1097-0258
    Date Added 6/3/2021, 10:26:33 AM
    Modified 6/3/2021, 10:27:26 AM

    Tags:

    • longitudinal
    • bayes
    • dropout
    • sensitivity-analysis
    • serial
    • missing-not-at-random
  • Selection bias found in interpreting analyses with missing data for the prehospital index for trauma

    Item Type Journal Article
    Author Lawrence Joseph
    Author Patrick Belisle
    Author Hala Tamim
    Author John S. Sampalis
    Date 2004
    Extra Citation Key: jos04sel tex.citeulike-article-id= 13265364 tex.posted-at= 2014-07-14 14:09:55 tex.priority= 0
    Volume 57
    Pages 147-153
    Publication J Clin Epi
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • simulation-setup
    • bayesian-methods
    • multiple-imputation
    • missing-data
    • polytomous-regression
    • single-imputation
    • robustness-of-multiple-imputation
    • trauma
  • Bayesian and mixed Bayesian/likelihood criteria for sample size determination

    Item Type Journal Article
    Author Lawrence Joseph
    Author Roxane Du Berger
    Author Patrick Bélisle
    Date 1997
    Extra Citation Key: jos97bayb tex.citeulike-article-id= 13264371 tex.posted-at= 2014-07-14 14:09:34 tex.priority= 0
    Volume 16
    Pages 769-781
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • study-design
    • sample-size-calculation
  • Interval-based versus decision theoretic criteria for the choice of sample size

    Item Type Journal Article
    Author Lawrence Joseph
    Author David B. Wolfson
    Date 1997
    Extra Citation Key: jos97int tex.citeulike-article-id= 13265275 tex.posted-at= 2014-07-14 14:09:53 tex.priority= 0
    Volume 46
    Pages 145-149
    Publication The Statistician
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • sample-size
    • highest-posterior-density
    • decision-theory
    • bayesian-sample-size-estimation
  • Bayesian sample size determination for normal means and differences between normal means

    Item Type Journal Article
    Author Lawrence Joseph
    Author Patrick Bélisle
    Date 1997
    Extra Citation Key: jos97baya tex.citeulike-article-id= 13265276 tex.posted-at= 2014-07-14 14:09:53 tex.priority= 0
    Volume 46
    Pages 209-226
    Publication The Statistician
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • sample-size
    • credible-interval
    • bayesian-sample-size-estimation
    • optimal-design
    • normal-case
  • Comparing Bayesian early stopping boundaries for phase II clinical trials

    Item Type Journal Article
    Author Liyun Jiang
    Author Fangrong Yan
    Author Peter F. Thall
    Author Xuelin Huang
    Abstract When designing phase II clinical trials, it is important to construct interim monitoring rules that achieve a balance between reliable early stopping for futility or safety and maintaining a high true positive probability (TPP), which is the probability of not stopping if the new treatment is truly safe and effective. We define and compare several methods for specifying early stopping boundaries as functions of interim sample size, rather than as fixed cut-offs, using Bayesian posterior probabilities as decision criteria. We consider boundaries with constant, linear, or exponential shapes. For design optimization criteria, we use the TPP and mean number of patients enrolled in the trial. Simulations to evaluate and compare the designs' operating characteristics under a range of scenarios show that, while there is no uniformly optimal boundary, an appropriately calibrated exponential shape maintains high TPP while limiting the number of patients assigned to a treatment with an inferior response rate or an excessive toxicity rate.
    Date 2020
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2046
    Accessed 8/2/2020, 7:19:31 AM
    Rights © 2020 John Wiley & Sons Ltd
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2046
    Volume n/a
    Publication Pharmaceutical Statistics
    DOI 10.1002/pst.2046
    Issue n/a
    ISSN 1539-1612
    Date Added 8/2/2020, 7:19:31 AM
    Modified 8/2/2020, 7:20:50 AM

    Tags:

    • bayes
    • futility
    • safety
    • sequential
    • phase-ii
    • stopping-boundaries
  • Statistical approaches to interim monitoring of medical trials: A review and commentary

    Item Type Journal Article
    Author Jennison
    Date 1990
    Extra Citation Key: jen90sta tex.citeulike-article-id= 13264360 tex.posted-at= 2014-07-14 14:09:33 tex.priority= 0
    Volume 5
    Pages 299-317
    Publication Stat Sci
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • sequential-methods
    • study-design-and-stopping-rules
    • bayesian-methods
    • confidence-limits
    • repeated-confidence-intervals

    Notes:

    • dealing with final estimates and confidence limits

  • Is the FDA too conservative or too aggressive?: A Bayesian decision analysis of clinical trial design

    Item Type Journal Article
    Author Leah Isakov
    Author Andrew W. Lo
    Author Vahid Montazerhodjat
    Abstract Implicit in the drug-approval process is a host of decisions—target patient population, control group, primary endpoint, sample size, follow-up period, etc.—all of which determine the trade-off between Type I and Type II error. We explore the application of Bayesian decision analysis (BDA) to minimize the expected cost of drug approval, where the relative costs of the two types of errors are calibrated using U.S. Burden of Disease Study 2010 data. The results for conventional fixed-sample randomized clinical-trial designs suggest that for terminal illnesses with no existing therapies such as pancreatic cancer, the standard threshold of 2.5% is substantially more conservative than the BDA-optimal threshold of 23.9% to 27.8%. For relatively less deadly conditions such as prostate cancer, 2.5% is more risk-tolerant or aggressive than the BDA-optimal threshold of 1.2% to 1.5%. We compute BDA-optimal sizes for 25 of the most lethal diseases and show how a BDA-informed approval process can incorporate all stakeholders’ views in a systematic, transparent, internally consistent, and repeatable manner.
    Date July 1, 2019
    Language en
    Short Title Is the FDA too conservative or too aggressive?
    Library Catalog ScienceDirect
    URL https://www.sciencedirect.com/science/article/pii/S0304407618302380
    Accessed 1/20/2022, 5:58:27 AM
    Volume 211
    Pages 117-136
    Publication Journal of Econometrics
    Series Annals Issue in Honor of Jerry A. Hausman
    DOI 10.1016/j.jeconom.2018.12.009
    Issue 1
    Journal Abbr Journal of Econometrics
    ISSN 0304-4076
    Date Added 1/20/2022, 5:58:27 AM
    Modified 1/20/2022, 5:59:22 AM

    Tags:

    • bayes
    • type-i-error
    • decision-theory
    • drug-development
    • alpha-spending
  • Bayesian Methods in Human Drug and Biological Products Development in CDER and CBER

    Item Type Journal Article
    Author Alexei C. Ionan
    Author Jennifer Clark
    Author James Travis
    Author Anup Amatya
    Author John Scott
    Author James P. Smith
    Author Somesh Chattopadhyay
    Author Mary Jo Salerno
    Author Mark Rothmann
    Abstract The Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER) of the U.S. Food and Drug Administration (FDA) have been leaders in protecting and promoting the U.S. public health by helping to ensure that safe and effective drugs and biological products are available in the United States for those who need them. The null hypothesis significance testing approach, along with other considerations, is typically used to demonstrate the effectiveness of a drug or biological product. The Bayesian framework presents an alternative approach to demonstrate the effectiveness of a treatment. This article discusses the Bayesian framework for drug and biological product development, highlights key settings in which Bayesian approaches may be appropriate, and provides recent examples of the use of Bayesian approaches within CDER and CBER.
    Date 2022-12-02
    Language en
    Library Catalog Springer Link
    URL https://doi.org/10.1007/s43441-022-00483-0
    Accessed 12/2/2022, 3:43:43 PM
    Publication Therapeutic Innovation & Regulatory Science
    DOI 10.1007/s43441-022-00483-0
    Journal Abbr Ther Innov Regul Sci
    ISSN 2168-4804
    Date Added 12/2/2022, 3:43:43 PM
    Modified 12/2/2022, 3:44:27 PM

    Tags:

    • bayes
    • drug-development

    Notes:

    • Examples of use of Bayes at FDA CDER and CBER

  • Bayesian Methods in Human Drug and Biological Products Development in CDER and CBER

    Item Type Journal Article
    Author Alexei C. Ionan
    Author Jennifer Clark
    Author James Travis
    Author Anup Amatya
    Author John Scott
    Author James P. Smith
    Author Somesh Chattopadhyay
    Author Mary Jo Salerno
    Author Mark Rothmann
    Date 05/2023
    Language en
    Library Catalog DOI.org (Crossref)
    URL https://link.springer.com/10.1007/s43441-022-00483-0
    Accessed 3/11/2024, 2:49:30 PM
    Volume 57
    Pages 436-444
    Publication Therapeutic Innovation & Regulatory Science
    DOI 10.1007/s43441-022-00483-0
    Issue 3
    Journal Abbr Ther Innov Regul Sci
    ISSN 2168-4790, 2168-4804
    Date Added 3/11/2024, 2:49:30 PM
    Modified 3/11/2024, 2:49:43 PM

    Tags:

    • bayes
    • drug-development
    • fda
  • Reporting Bayesian analyses of clinical trials

    Item Type Journal Article
    Author Michael D. Hughes
    Date 1993
    Extra Citation Key: hug93rep tex.citeulike-article-id= 13264343 tex.posted-at= 2014-07-14 14:09:33 tex.priority= 0
    Volume 12
    Pages 1651-1663
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • study-design
    • skeptical-priors
  • Hierarchical Bayesian semiparametric procedures for logistic regression

    Item Type Journal Article
    Author John S. J. Hsu
    Author Tom Leonard
    Date 1997
    Extra Citation Key: hsu97hie tex.citeulike-article-id= 13264339 tex.posted-at= 2014-07-14 14:09:33 tex.priority= 0
    Volume 84
    Pages 85-93
    Publication Biometrika
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • hierarchical-model
    • semiparametric-model
    • bayesian-logistic-model
    • bayesian-smoothing
  • Scientific Reasoning: The Bayesian Approach

    Item Type Book
    Author C. Howson
    Author P. Urbach
    Date 1989
    Extra Citation Key: how89sci tex.citeulike-article-id= 13264332 tex.posted-at= 2014-07-14 14:09:33 tex.priority= 0
    Place La Salle, IL
    Publisher Open Court
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • scientific-reasoning

    Notes:

    • Written by two philosophers, and has no pretense of being objective.

  • The 2 2 table: A discussion from a Bayesian viewpoint

    Item Type Journal Article
    Author J. V. Howard
    Date 1998
    Extra Citation Key: how982x2 tex.citeulike-article-id= 13264333 tex.posted-at= 2014-07-14 14:09:33 tex.priority= 0
    Volume 13
    Pages 351-367
    Publication Stat Sci
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • odds-ratio
    • fishers-exact-test
    • conditioning
    • necessity-for-dependent-priors
  • Designing a cost-effective clinical trial

    Item Type Journal Article
    Author John C. Hornberger
    Author Byron W. Brown
    Author Jerry Halpern
    Date 1995
    URL http://dx.doi.org/10.1002/sim.4780142008
    Extra Citation Key: hor95des tex.citeulike-article-id= 13264321 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.4780142008 tex.posted-at= 2014-07-14 14:09:33 tex.priority= 0
    Volume 14
    Pages 2249-2259
    Publication Stat Med
    DOI 10.1002/sim.4780142008
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • study-design
    • cost-benefit
    • bayesian-methods
    • cost-effectiveness
    • c-e
    • loss-function
    • economics
    • target-population
  • Improving clinical trials using Bayesian adaptive designs: a breast cancer example

    Item Type Journal Article
    Author Wei Hong
    Author Sue-Anne McLachlan
    Author Melissa Moore
    Author Robert K. Mahar
    Abstract To perform virtual re-executions of a breast cancer clinical trial with a time-to-event outcome to demonstrate what would have happened if the trial had used various Bayesian adaptive designs instead.
    Date 2022-05-04
    Short Title Improving clinical trials using Bayesian adaptive designs
    Library Catalog BioMed Central
    URL https://doi.org/10.1186/s12874-022-01603-y
    Accessed 5/5/2022, 6:25:41 AM
    Volume 22
    Pages 133
    Publication BMC Medical Research Methodology
    DOI 10.1186/s12874-022-01603-y
    Issue 1
    Journal Abbr BMC Medical Research Methodology
    ISSN 1471-2288
    Date Added 5/5/2022, 6:25:41 AM
    Modified 5/5/2022, 6:26:44 AM

    Tags:

    • bayes
    • rct
    • simulation
    • adaptive
    • adaptive-clinical-trials

    Notes:

    • Promising adaptive RCT simulation tools.  Obtained excellent frequentist results, but mislabeled the approach as Bayesian.  It is a hybrid Bayesian/frequentist approach.  A pure Bayesian approach would have had even better performance.

  • Being sceptical about meta-analyses: a Bayesian perspective on magnesium trials in myocardial infarction

    Item Type Journal Article
    Author Julian PT Higgins
    Author David J. Spiegelhalter
    Abstract Abstract. Background There has been extensive discussion of the apparent conflict between meta-analyses and a mega-trial investigating the benefits of intraven
    Date 2002/02/01
    Language en
    Short Title Being sceptical about meta-analyses
    Library Catalog academic.oup.com
    URL https://academic.oup.com/ije/article/31/1/96/655931
    Accessed 9/7/2019, 1:08:44 PM
    Volume 31
    Pages 96-104
    Publication International Journal of Epidemiology
    DOI 10.1093/ije/31.1.96
    Issue 1
    Journal Abbr Int J Epidemiol
    ISSN 0300-5771
    Date Added 9/7/2019, 1:08:44 PM
    Modified 9/7/2019, 1:09:23 PM

    Tags:

    • meta-analysis
    • empirical-bayes
    • hierarchical-model
    • hierarchical-bayes-model

    Notes:

    • magnesium

  • Bayesian Analytical Methods in Cardiovascular Clinical Trials: Why, When, and How

    Item Type Journal Article
    Author Samuel Heuts
    Author Michal J. Kawczynski
    Author Ahmed Sayed
    Author Sarah M. Urbut
    Author Arthur M. Albuquerque
    Author John M. Mandrola
    Author Sanjay Kaul
    Author Frank E. Harrell
    Author Andrea Gabrio
    Author James M. Brophy
    Date 11/2024
    Language en
    Short Title Bayesian Analytical Methods in Cardiovascular Clinical Trials
    Library Catalog DOI.org (Crossref)
    URL https://linkinghub.elsevier.com/retrieve/pii/S0828282X24011309
    Accessed 11/12/2024, 7:29:15 AM
    Pages S0828282X24011309
    Publication Canadian Journal of Cardiology
    DOI 10.1016/j.cjca.2024.11.002
    Journal Abbr Canadian Journal of Cardiology
    ISSN 0828282X
    Date Added 11/12/2024, 7:29:15 AM
    Modified 11/12/2024, 7:29:34 AM

    Tags:

    • bayes
    • rct
    • teaching-mds
  • Bayesian modeling of multiple episode occurrence and severity with a terminating event

    Item Type Journal Article
    Author Amy H. Herring
    Author Juan Yang
    Date 2007
    Extra Citation Key: her07bay tex.citeulike-article-id= 13265599 tex.posted-at= 2014-07-14 14:10:00 tex.priority= 0
    Volume 63
    Pages 381-388
    Publication Biometrics
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • data-augmentation
    • dependent-censoring
    • bayesian-analysis
    • dynamic-latent-variables
    • frequency-and-intensity-of-episodes
    • multiple-event-times
    • pregnancy
  • Polygenic scores via penalized regression on summary statistics.

    Item Type Journal Article
    Author Timothy Shin Heng
    Author Robert Milan
    Author Shing Wan
    Author Xueya Zhou
    Author Pak Chung
    Abstract Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating PGS have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can use LD information available elsewhere to supplement such analyses. To answer this question, we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and P-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred.
    Date 2017-09
    URL http://view.ncbi.nlm.nih.gov/pubmed/28480976
    Extra Citation Key: mak17pol tex.citeulike-article-id= 14595150 tex.citeulike-linkout-0= http://view.ncbi.nlm.nih.gov/pubmed/28480976 tex.citeulike-linkout-1= http://www.hubmed.org/display.cgi?uids=28480976 tex.pmid= 28480976 tex.posted-at= 2018-05-27 20:09:41 tex.priority= 2
    Volume 41
    Pages 469-480
    Publication Gen Epi
    Issue 6
    ISSN 1098-2272
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • idiot-bayes
    • penalized-mle
    • penalization
    • lasso
    • genetic
    • genetic-association-studies
    • genetics

    Notes:

    • discusses naive Bayes (idiot Bayes). Decomposes the penalized lasso likelihood and shows how pieces of it can be estimated from other sources. Discusses loss of accuracy of naive Bayes if predictors are correlated.

  • Problems and prediction in survival-data analysis

    Item Type Journal Article
    Author Robin Henderson
    Date 1995
    Extra Citation Key: hen95pro tex.citeulike-article-id= 13264286 tex.posted-at= 2014-07-14 14:09:32 tex.priority= 0
    Volume 14
    Pages 161-184
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • ph-model
    • cox-model
    • general
    • predictive-accuracy
    • partial-likelihood
    • prediction
    • explained-variation
    • bayesian-estimation
    • clear-explanation
    • loss-function-characteristics
    • review-of-survival-analysis
    • semiparametric-aft-models
    • survival-estimation

    Notes:

    • predicting survival times and probabilities from Cox model

  • Bayesian interim analysis of phase II cancer clinical trials

    Item Type Journal Article
    Author Daniel F. Heitjan
    Date 1997
    Extra Citation Key: hei97bay tex.citeulike-article-id= 13264283 tex.posted-at= 2014-07-14 14:09:32 tex.priority= 0
    Volume 16
    Pages 1791-1802
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • study-design
    • choice-of-prior
    • phase-ii
    • use-of-theoretical-expert
  • On the accuracy of classifying hospitals on their performance measures

    Item Type Journal Article
    Author Yulei He
    Author Frederic Selck
    Author Sharon-Lise T. Normand
    Abstract The evaluation, comparison, and public report of health care provider performance is essential to improving the quality of health care. Hospitals, as one type of provider, are often classified into quality tiers (e.g., top or suboptimal) based on their performance data for various purposes. However, potential misclassification might lead to detrimental effects for both consumers and payers. Although such risk has been highlighted by applied health services researchers, a systematic investigation of statistical approaches has been lacking. We assess and compare the expected accuracy of several commonly used classification methods: unadjusted hospital-level averages, shrinkage estimators under a random-effects model accommodating between-hospital variation, and two others based on posterior probabilities. Assuming that performance data follow a classic one-way random-effects model with unequal sample size per hospital, we derive accuracy formulae for these classification approaches and gain insight into how the misclassification might be affected by various factors such as reliability of the data, hospital-level sample size distribution, and cutoff values between quality tiers. The case of binary performance data is also explored using Monte Carlo simulation strategies. We apply the methods to real data and discuss the practical implications.
    Date 2014-03
    URL http://dx.doi.org/10.1002/sim.6012
    Extra Citation Key: he14acc tex.citeulike-article-id= 13448163 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.6012 tex.day= 30 tex.posted-at= 2014-11-29 16:33:28 tex.priority= 2
    Volume 33
    Pages 1081-1103
    Publication Stat Med
    DOI 10.1002/sim.6012
    Issue 7
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • provider-profiling
    • accuracy
    • reliability
  • Ivermectin for COVID-19 in adults in the community (PRINCIPLE): an open, randomised, controlled, adaptive platform trial of short- and longer-term outcomes

    Item Type Journal Article
    Author Gail Hayward
    Author Ly-Mee Yu
    Author Paul Little
    Author Oghenekome Gbinigie
    Author Milensu Shanyinde
    Author Victoria Harris
    Author Jienchi Dorward
    Author Benjamin R Saville
    Author Nicholas Berry
    Author Philip H Evans
    Author Nicholas Pb Thomas
    Author Mahendra G Patel
    Author Duncan Richards
    Author Oliver Van Hecke
    Author Michelle A Detry
    Author Christina Saunders
    Author Mark Fitzgerald
    Author Jared Robinson
    Author Charlotte Latimer-Bell
    Author Julie Allen
    Author Emma Ogburn
    Author Jenna Grabey
    Author Simon De Lusignan
    Author Fd Richard Hobbs
    Author Christopher C Butler
    Date 2/2024
    Language en
    Short Title Ivermectin for COVID-19 in adults in the community (PRINCIPLE)
    Library Catalog DOI.org (Crossref)
    URL https://linkinghub.elsevier.com/retrieve/pii/S0163445324000641
    Accessed 3/2/2024, 8:03:08 AM
    Pages 106130
    Publication Journal of Infection
    DOI 10.1016/j.jinf.2024.106130
    Journal Abbr Journal of Infection
    ISSN 01634453
    Date Added 3/2/2024, 8:03:08 AM
    Modified 3/2/2024, 8:04:03 AM

    Tags:

    • bayes
    • interim-analysis
    • futility
    • interim-monitoring
    • clinical-significance
    • ivermectin
  • Bayesian analysis for a single 2 2 table

    Item Type Journal Article
    Author Lobat Hashemi
    Author Balgobin Nandram
    Author Robert Goldberg
    Date 1997
    Extra Citation Key: has97bay tex.citeulike-article-id= 13264272 tex.posted-at= 2014-07-14 14:09:32 tex.priority= 0
    Volume 16
    Pages 1311-1328
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • odds-ratio
    • binomial
    • 2x2-table
    • risk-ratio
  • Bayesian Inference Is Unaffected by Selection: Fact or Fiction?

    Item Type Journal Article
    Author David A. Harville
    Abstract The problem considered is that of making inferences about the value of a parameter vector θ based on the value of an observable random vector y that is subject to selection of the form y ∈ S (for a known subset S). According to conventional wisdom, a Bayesian approach (unlike a frequentist approach) requires no adjustment for selection, which is generally regarded as counterintuitive and even paradoxical. An alternative considered herein consists (when taking a Bayesian approach in the face of selection) of basing the inferences for the value of θ on the posterior distribution derived from the conditional (on y ∈ S ) joint distribution of y and θ . That leads to an adjustment in the likelihood function that is reinterpretable as an adjustment to the prior distribution and ultimately leads to a different posterior distribution. And it serves to make the inferences specific to settings that are subject to selection of the same kind as the setting that gave rise to the data. Moreover, even in the absence of any real selection, this approach can be used to make the inferences specific to a meaningful subset of y-values.
    Date 2021
    Short Title Bayesian Inference Is Unaffected by Selection
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/00031305.2020.1858963
    Accessed 1/6/2021, 11:05:03 AM
    Extra Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00031305.2020.1858963
    Volume 0
    Pages 1-7
    Publication The American Statistician
    Series mv Ba
    DOI 10.1080/00031305.2020.1858963
    Issue 0
    ISSN 0003-1305
    Date Added 1/6/2021, 11:05:03 AM
    Modified 1/6/2021, 11:19:49 AM

    Tags:

    • bayes
    • prior
    • selection-bias
    • multiplicity
  • Using full probability models to compute probabilities of actual interest to decision-makers

    Item Type Journal Article
    Author Frank E. Harrell
    Author Tina Shih
    Date 2001
    Extra Citation Key: har01usi tex.citeulike-article-id= 13265138 tex.posted-at= 2014-07-14 14:09:50 tex.priority= 0
    Volume 17
    Pages 17-26
    Publication Int J Tech Assess Hlth Care
    Date Added 7/7/2018, 1:38:33 PM
    Modified 8/5/2021, 11:12:37 AM

    Tags:

    • bayes
    • bayesian
    • decision-support-techniques
    • special-issue-basesian-approach-to-technology-assessment-and-decision-making
  • A case study in comparing therapies involving informative drop-out, non-ignorable non-compliance and repeated measurements

    Item Type Journal Article
    Author T. Härkänen
    Author P. Knekt
    Author E. Virtala
    Author O. Lindfors
    Author The Helsinki Psychotherapy Study Group
    Date 2005
    Extra Citation Key: har05cas tex.citeulike-article-id= 13265454 tex.posted-at= 2014-07-14 14:09:57 tex.priority= 0
    Volume 24
    Pages 3773-3787
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • bayesian-inference
    • repeated-measurements
    • serial-data
    • nonrandom-dropout
    • non-ignorable-missing-data
    • non-ignorable-non-compliance
    • surrogate

    Notes:

    • use of surrogate data collected during interviews of patients who dropped out

  • A Bayesian Interpretation of a Pediatric Cardiac Arrest Trial (THAPCA-OH)

    Item Type Journal Article
    Author Michael O. Harhay
    Author Bryan S. Blette
    Author Anders Granholm
    Author Frank W. Moler
    Author Fernando G. Zampieri
    Author Ewan C. Goligher
    Author Monique M. Gardner
    Author Alexis A. Topjian
    Author Nadir Yehya
    Abstract BACKGROUND Pediatric out-of-hospital cardiac arrest results in high morbidity and mortality. Currently, there are no recommended therapies beyond supportive care. The THAPCA-OH (Therapeutic Hypothermia after Pediatric Cardiac Arrest Out-of-Hospital) trial compared hypothermia (33.0°C) with normothermia (36.8°C) in 295 children. Good neurobehavioral outcome and survival at 1 year were higher in the hypothermia group (20 vs. 12% and 38 vs. 29%, respectively). These differences did not meet the planned statistical threshold of P<0.05. To ensure that a potentially efficacious therapy is not prematurely discarded, we reassessed THAPCA-OH using a Bayesian statistical perspective. METHODS We performed a Bayesian analysis, interpreting the trial in probabilistic terms (i.e., the probability that therapeutic hypothermia had any benefit, and overall absolute improvements greater than 2%, 5%, and 10% for 1-year neurobehavioral outcome and survival). Our primary analyses used noninformative priors, meaning that the analyses were based on the observed trial data without any information added by the priors. In the absence of pediatric trials to derive informative prior distributions, we used: (1) downweighted priors from adult trials; and (2) a previously published set of critical care priors that span benefit, equipoise, and harm. RESULTS In the primary analyses, the probability of any benefit from hypothermia was 94% for both the neurobehavioral outcome and survival at 1 year. For both outcomes, the probability of benefit was >75% for all informative prior integrations with the THAPCA-OH results, except those with the most pessimistic priors. CONCLUSIONS There is a high probability that hypothermia provides a modest benefit in neurobehavioral outcome and survival at 1 year. (ClinicalTrials.gov number, NCT00878644.)
    Date 2022-12-27
    Library Catalog evidence.nejm.org (Atypon)
    URL https://evidence.nejm.org/doi/10.1056/EVIDoa2200196
    Accessed 12/30/2022, 3:21:36 AM
    Extra Publisher: Massachusetts Medical Society
    Volume 2
    Pages EVIDoa2200196
    Publication NEJM Evidence
    DOI 10.1056/EVIDoa2200196
    Issue 1
    Date Added 12/30/2022, 3:21:36 AM
    Modified 12/30/2022, 3:22:54 AM

    Tags:

    • bayes
    • rct
    • teaching
    • teaching-mds
    • borrow-information
  • Multiple imputation for correcting verification bias

    Item Type Journal Article
    Author Ofer Harel
    Author Xiao-Hua Zhou
    Date 2006
    Extra Citation Key: har06mul tex.citeulike-article-id= 13265543 tex.posted-at= 2014-07-14 14:09:58 tex.priority= 0
    Volume 26
    Pages 3769-3786
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • diagnosis
    • verification-bias
    • workup-bias
    • bayesian-methods
    • imputation
    • missing-data
    • software
    • referral-bias

    Notes:

    • hypercritical letter to the editor and rejoinder, 26:3046-3050; de Groot <i>et al</i> demonstrated that the Harel and Zhou paper is invalid in 27:5880-5889 after the journal mistakenly published a letter to the editor by the original authors while the de Groot <i>et al</i> paper was in press. See deg08mul

  • Utilizing Bayesian predictive power in clinical trial design

    Item Type Journal Article
    Author Ofir Harari
    Author Grace Hsu
    Author Louis Dron
    Author Jay J. H. Park
    Author Kristian Thorlund
    Author Edward J. Mills
    Abstract The Bayesian paradigm provides an ideal platform to update uncertainties and carry them over into the future in the presence of data. Bayesian predictive power (BPP) reflects our belief in the eventual success of a clinical trial to meet its goals. In this paper we derive mathematical expressions for the most common types of outcomes, to make the BPP accessible to practitioners, facilitate fast computations in adaptive trial design simulations that use interim futility monitoring, and propose an organized BPP-based phase II-to-phase III design framework.
    Date 2020
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2073
    Accessed 10/10/2020, 11:03:58 AM
    Rights © 2020 John Wiley & Sons Ltd
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2073
    Volume n/a
    Publication Pharmaceutical Statistics
    DOI 10.1002/pst.2073
    Issue n/a
    ISSN 1539-1612
    Date Added 10/10/2020, 11:03:58 AM
    Modified 10/10/2020, 11:04:56 AM

    Tags:

    • bayes
    • rct
    • study-design
    • study-design-and-stopping-rules
    • design
    • futility
    • design-of-rct
  • Utilizing Bayesian predictive power in clinical trial design

    Item Type Journal Article
    Author Ofir Harari
    Author Grace Hsu
    Author Louis Dron
    Author Jay J. H. Park
    Author Kristian Thorlund
    Author Edward J. Mills
    Abstract The Bayesian paradigm provides an ideal platform to update uncertainties and carry them over into the future in the presence of data. Bayesian predictive power (BPP) reflects our belief in the eventual success of a clinical trial to meet its goals. In this paper we derive mathematical expressions for the most common types of outcomes, to make the BPP accessible to practitioners, facilitate fast computations in adaptive trial design simulations that use interim futility monitoring, and propose an organized BPP-based phase II-to-phase III design framework.
    Date 2021
    Language en
    Library Catalog Wiley Online Library
    URL http://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2073
    Accessed 3/7/2021, 9:50:48 AM
    Rights © 2020 John Wiley & Sons Ltd
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2073
    Volume 20
    Pages 256-271
    Publication Pharmaceutical Statistics
    DOI https://doi.org/10.1002/pst.2073
    Issue 2
    ISSN 1539-1612
    Date Added 3/7/2021, 9:50:48 AM
    Modified 3/7/2021, 9:51:20 AM

    Tags:

    • bayes
    • rct
    • predictive-power
  • Utilizing Bayesian predictive power in clinical trial design

    Item Type Journal Article
    Author Ofir Harari
    Author Grace Hsu
    Author Louis Dron
    Author Jay J. H. Park
    Author Kristian Thorlund
    Author Edward J. Mills
    Date 2020
    URL https://dx.doi.org/10.1002/pst.2073
    Extra Publisher: Wiley
    Publication Pharmaceutical Statistics
    DOI 10.1002/pst.2073
    Journal Abbr Pharmaceutical Statistics
    ISSN 1539-1604
    Date Added 10/30/2020, 9:10:04 AM
    Modified 10/30/2020, 9:10:44 AM

    Tags:

    • bayes
    • rct
    • futility
    • predictive-distribution
    • predictive-power
  • Coherent Tests for Interval Null Hypotheses

    Item Type Journal Article
    Author Spencer Hansen
    Author Ken Rice
    Abstract In a celebrated 1996 article, Schervish showed that, for testing interval null hypotheses, tests typically viewed as optimal can be logically incoherent. Specifically, one may fail to reject a specific interval null, but nevertheless—testing at the same level with the same data—reject a larger null, in which the original one is nested. This result has been used to argue against the widespread practice of viewing p-values as measures of evidence. In the current work we approach tests of interval nulls using simple Bayesian decision theory, and establish straightforward conditions that ensure coherence in Schervish’s sense. From these, we go on to establish novel frequentist criteria—different to Type I error rate—that, when controlled at fixed levels, give tests that are coherent in Schervish’s sense. The results suggest that exploring frequentist properties beyond the familiar Neyman–Pearson framework may ameliorate some of statistical testing’s well-known problems.
    Date 2022-03-08
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/00031305.2022.2050299
    Accessed 4/9/2022, 11:06:10 AM
    Extra Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00031305.2022.2050299
    Volume 0
    Pages 1-9
    Publication The American Statistician
    DOI 10.1080/00031305.2022.2050299
    Issue 0
    ISSN 0003-1305
    Date Added 4/9/2022, 11:06:10 AM
    Modified 4/9/2022, 11:07:21 AM

    Tags:

    • bayes
    • hypothesis-testing
    • coherent
    • inference
    • interval-null

    Notes:

    • New way of looking at Schervish interval null hypothesis testing incoherence example

      Tweet: Interval H₀:θ∊[a,b] tests can be incoherent e.g. p-value can ↓ if a ↓. Coherent only if do not respect α.  Bayes makes all this simple: posterior P(θ∈[a,b]) will be coherent, i.e., will ↑ as a ↓.  libkey.io/10.1080/00031305.2022.2050299

  • Bayesian lasso regression

    Item Type Journal Article
    Author Chris Hans
    Date 2009
    Extra Citation Key: han09bay tex.citeulike-article-id= 13265788 tex.posted-at= 2014-07-14 14:10:04 tex.priority= 0
    Volume 96
    Pages 835-845
    Publication Biometrika
    Issue 4
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • lasso
    • bayesian-lasso
    • double-exponential-distribution
    • posterior-predictive-distribution

    Notes:

    • advantage of using predicted mean instead of mode

  • Improving the assessment of the probability of success in late stage drug development

    Item Type Journal Article
    Author Lisa V. Hampson
    Author Björn Bornkamp
    Author Björn Holzhauer
    Author Joseph Kahn
    Author Markus R. Lange
    Author Wen-Lin Luo
    Author Giovanni Della Cioppa
    Author Kelvin Stott
    Author Steffen Ballerstedt
    Abstract There are several steps to confirming the safety and efficacy of a new medicine. A sequence of trials, each with its own objectives, is usually required. Quantitative risk metrics can be useful for informing decisions about whether a medicine should transition from one stage of development to the next. To obtain an estimate of the probability of regulatory approval, pharmaceutical companies may start with industry-wide success rates and then apply to these subjective adjustments to reflect program-specific information. However, this approach lacks transparency and fails to make full use of data from previous clinical trials. We describe a quantitative Bayesian approach for calculating the probability of success (PoS) at the end of phase II which incorporates internal clinical data from one or more phase IIb studies, industry-wide success rates, and expert opinion or external data if needed. Using an example, we illustrate how PoS can be calculated accounting for differences between the phase II data and future phase III trials, and discuss how the methods can be extended to accommodate accelerated drug development pathways.
    Date 2021
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2179
    Accessed 12/15/2021, 8:31:12 PM
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2179
    Volume n/a
    Publication Pharmaceutical Statistics
    DOI 10.1002/pst.2179
    Issue n/a
    ISSN 1539-1612
    Date Added 12/15/2021, 8:31:12 PM
    Modified 12/15/2021, 8:32:07 PM

    Tags:

    • bayes
    • drug-development
    • drug-development-program
    • probability-of-success
  • The performance of random coefficient regression in accounting for residual confounding

    Item Type Journal Article
    Author Paul Gustafson
    Author Sander Greenland
    Date 2006
    Extra Citation Key: gus06per tex.citeulike-article-id= 13265538 tex.posted-at= 2014-07-14 14:09:58 tex.priority= 0
    Volume 62
    Pages 760-768
    Publication Biometrics
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • confounding
    • random-effects
    • observational-studies
    • bayesian-analysis
    • bias
    • epidemiology
    • mixed-models
    • residual-confounding
    • heirarchical-models
    • identifiability
    • nutritional-epidemiology
    • random-coefficient-regression
    • random-slopes
  • Bayesian sample size determination in non-sequential clinical trials: Statistical aspects and some regulatory considerations

    Item Type Journal Article
    Author Jean-Marie Grouin
    Author Maylis Coste
    Author Pierre Bunouf
    Author Bruno Lecoutre
    Date 2007
    Extra Citation Key: gro07bay tex.citeulike-article-id= 13265637 tex.posted-at= 2014-07-14 14:10:00 tex.priority= 0
    Volume 26
    Pages 4914-4924
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • sample-size
    • bayesian-methods
    • regulatory-viewpoint
    • credible-interval-for-sample-size
    • distribution-of-sample-sizes
  • A unified method for monitoring and analysing controlled trials

    Item Type Journal Article
    Author Jason Grossman
    Author Mahesh K. B. Parmar
    Author David J. Spiegelhalter
    Date 1994
    Extra Citation Key: gro94uni tex.citeulike-article-id= 13264189 tex.posted-at= 2014-07-14 14:09:30 tex.priority= 0
    Volume 13
    Pages 1815-1826
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • study-design
    • bayesian-methods
    • sequential-testing
    • early-termination
  • Extending a Bayesian Analysis of the Two-Period Crossover to Accommodate Missing Data

    Item Type Journal Article
    Author Andrew P. Grieve
    Abstract A procedure is developed for performing a Bayesian analysis of a simple two-period crossover design when some observations are missing in one, or both, treatment periods. The approach requires the numerical evaluation of a single integral to be performed for each posterior marginal distribution of interest. The relative efficiency of this approach to one in which patients with missing data are excluded is investigated.
    Date 1995
    Archive JSTOR
    Library Catalog JSTOR
    URL www.jstor.org/stable/2337407
    Accessed 12/7/2019, 6:09:06 PM
    Volume 82
    Pages 277-286
    Publication Biometrika
    DOI 10.2307/2337407
    Issue 2
    ISSN 0006-3444
    Date Added 12/7/2019, 6:09:06 PM
    Modified 12/7/2019, 6:10:07 PM

    Tags:

    • bayes
    • rct
    • crossover
    • missing
    • cross-over-trials
  • A Bayesian Analysis of the Two-Period Crossover Design for Clinical Trials

    Item Type Journal Article
    Author A. P. Grieve
    Abstract Statisticians have been critical of the use of the two-period crossover designs for clinical trials because the estimate of the treatment difference is biased when the carryover effects of the two treatments are not equal. In the standard approach, if the null hypothesis of equal carryover effects is not rejected, data from both periods are used to estimate and test for treatment differences; if the null hypothesis is rejected, data from the first period alone are used. A Bayesian analysis based on the Bayes factor against unequal carryover effects is given. Although this Bayesian approach avoids the "all-or-nothing" decision inherent in the standard approach, it recognizes that with small trials it is difficult to provide unequivocal evidence that the carryover effects of the two treatments are equal, and thus that the interpretation of the difference between treatment effects is highly dependent on a subjective assessment of the reality or not of equal carryover effects.
    Date 1985
    Archive JSTOR
    Library Catalog JSTOR
    URL www.jstor.org/stable/2530969
    Accessed 12/7/2019, 6:05:32 PM
    Volume 41
    Pages 979-990
    Publication Biometrics
    DOI 10.2307/2530969
    Issue 4
    ISSN 0006-341X
    Date Added 12/7/2019, 6:05:33 PM
    Modified 12/7/2019, 6:06:14 PM

    Tags:

    • bayes
    • rct
    • crossover
    • cross-over-trials
  • Issues for statisticians in pharmaco-economic evaluations

    Item Type Journal Article
    Author A. P. Grieve
    Date 1998
    Extra Citation Key: gri98iss tex.citeulike-article-id= 13264187 tex.posted-at= 2014-07-14 14:09:30 tex.priority= 0
    Volume 17
    Pages 1715-1723
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • study-design
    • analysis-of-cost
    • cost-effectiveness
  • When should epidemiologic regressions use random coefficients?

    Item Type Journal Article
    Author Sander Greenland
    Date 2000
    URL http://dx.doi.org/10.1111/j.0006-341X.2000.00915.x
    Extra Citation Key: gre00whe tex.citeulike-article-id= 13265446 tex.citeulike-linkout-0= http://dx.doi.org/10.1111/j.0006-341X.2000.00915.x tex.posted-at= 2014-07-14 14:09:57 tex.priority= 0
    Volume 56
    Pages 915-921
    Publication Biometrics
    DOI 10.1111/j.0006-341X.2000.00915.x
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • causal-inference
    • bayesian-methods
    • shrinkage
    • mixed-models
    • multilevel-modeling
    • variance-components
    • random-coefficient-regression
    • empirical-bayes-estimators
    • epidemiologic-method
    • hierarchical-regression

    Notes:

    • use of statistics in epidemiology is largely primitive;stepwise variable selection on confounders leaves important confounders uncontrolled;composition matrix;example with far too many significant predictors with many regression coefficients absurdly inflated when overfit;lack of evidence for dietary effects mediated through constituents;shrinkage instead of variable selection;larger effect on confidence interval width than on point estimates with variable selection;uncertainty about variance of random effects is just uncertainty about prior opinion;estimation of variance is pointless;instead the analysis should be repeated using different values;"if one feels compelled to estimate $\tau^{2}$, I would recommend giving it a proper prior concentrated amount contextually reasonable values";claim about ordinary MLE being unbiased is misleading because it assumes the model is correct and is the only model entertained;shrinkage towards compositional model;"models need to be complex to capture uncertainty about the relations...an honest uncertainty assessment requires parameters for all effects that we know may be present. This advice is implicit in an antiparsimony principle often attributed to L. J. Savage 'All models should be as big as an elephant (see Draper, 1995)'". See also gus06per.

  • Robust Bayesian methods for monitoring clinical trials

    Item Type Journal Article
    Author Joel B. Greenhouse
    Author Larry Wasserman
    Date 1995
    Extra Citation Key: gre95rob tex.citeulike-article-id= 13264182 tex.posted-at= 2014-07-14 14:09:30 tex.priority= 0
    Volume 14
    Pages 1379-1391
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-methods
    • sequential-monitoring
    • robustness-to-choice-of-prior
  • Dexamethasone 12 mg versus 6 mg for patients with COVID-19 and severe hypoxaemia: a pre-planned, secondary Bayesian analysis of the COVID STEROID 2 trial

    Item Type Journal Article
    Author Anders Granholm
    Author Marie Warrer Munch
    Author Sheila Nainan Myatra
    Author Bharath Kumar Tirupakuzhi Vijayaraghavan
    Author Maria Cronhjort
    Author Rebecka Rubenson Wahlin
    Author Stephan M. Jakob
    Author Luca Cioccari
    Author Maj-Brit Nørregaard Kjær
    Author Gitte Kingo Vesterlund
    Author Tine Sylvest Meyhoff
    Author Marie Helleberg
    Author Morten Hylander Møller
    Author Thomas Benfield
    Author Balasubramanian Venkatesh
    Author Naomi E. Hammond
    Author Sharon Micallef
    Author Abhinav Bassi
    Author Oommen John
    Author Vivekanand Jha
    Author Klaus Tjelle Kristiansen
    Author Charlotte Suppli Ulrik
    Author Vibeke Lind Jørgensen
    Author Margit Smitt
    Author Morten H. Bestle
    Author Anne Sofie Andreasen
    Author Lone Musaeus Poulsen
    Author Bodil Steen Rasmussen
    Author Anne Craveiro Brøchner
    Author Thomas Strøm
    Author Anders Møller
    Author Mohd Saif Khan
    Author Ajay Padmanaban
    Author Jigeeshu Vasishtha Divatia
    Author Sanjith Saseedharan
    Author Kapil Borawake
    Author Farhad Kapadia
    Author Subhal Dixit
    Author Rajesh Chawla
    Author Urvi Shukla
    Author Pravin Amin
    Author Michelle S. Chew
    Author Christian Aage Wamberg
    Author Christian Gluud
    Author Theis Lange
    Author Anders Perner
    Abstract We compared dexamethasone 12 versus 6 mg daily for up to 10 days in patients with coronavirus disease 2019 (COVID-19) and severe hypoxaemia in the international, randomised, blinded COVID STEROID 2 trial. In the primary, conventional analyses, the predefined statistical significance thresholds were not reached. We conducted a pre-planned Bayesian analysis to facilitate probabilistic interpretation.
    Date 2021-11-10
    Language en
    Short Title Dexamethasone 12 mg versus 6 mg for patients with COVID-19 and severe hypoxaemia
    Library Catalog Springer Link
    URL https://doi.org/10.1007/s00134-021-06573-1
    Accessed 11/12/2021, 11:07:59 AM
    Publication Intensive Care Medicine
    DOI 10.1007/s00134-021-06573-1
    Journal Abbr Intensive Care Med
    ISSN 1432-1238
    Date Added 11/12/2021, 11:07:59 AM
    Modified 11/12/2021, 11:09:06 AM

    Tags:

    • bayes
    • rct
    • teaching-mds
  • Effects of sceptical priors on the performance of adaptive clinical trials with binary outcomes.

    Item Type Journal Article
    Author Anders Granholm
    Author Theis Lange
    Author Michael O. Harhay
    Author Anders Perner
    Author Morten Hylander Møller
    Author Benjamin Skov Kaas‐Hansen
    Abstract Abstract It is unclear how sceptical priors impact adaptive trials. We assessed the influence of priors expressing a spectrum of scepticism on the performance of several Bayesian, multi‐stage, adaptive clinical trial designs using binary outcomes under different clinical scenarios. Simulations were conducted using fixed stopping rules and stopping rules calibrated to keep type 1 error rates at approximately 5%. We assessed total sample sizes, event rates, event counts, probabilities of conclusiveness and selecting the best arm, root mean squared errors (RMSEs) of the estimated treatment effect in the selected arms, and ideal design percentages (IDPs; which combines arm selection probabilities, power, and consequences of selecting inferior arms), with RMSEs and IDPs estimated in conclusive trials only and after selecting the control arm in inconclusive trials. Using fixed stopping rules, increasingly sceptical priors led to larger sample sizes, more events, higher IDPs in simulations ending in superiority, and lower RMSEs, lower probabilities of conclusiveness/selecting the best arm, and lower IDPs when selecting controls in inconclusive simulations. With calibrated stopping rules, the effects of increased scepticism on sample sizes and event counts were attenuated, and increased scepticism increased the probabilities of conclusiveness/selecting the best arm and IDPs when selecting controls in inconclusive simulations without substantially increasing sample sizes. Results from trial designs with gentle adaptation and non‐informative priors resembled those from designs with more aggressive adaptation using weakly‐to‐moderately sceptical priors. In conclusion, the use of somewhat sceptical priors in adaptive trial designs with binary outcomes seems reasonable when considering multiple performance metrics simultaneously.
    Date 2024-03-29
    Language en
    Library Catalog DOI.org (Crossref)
    URL https://onlinelibrary.wiley.com/doi/10.1002/pst.2387
    Accessed 4/2/2024, 7:37:04 AM
    Pages pst.2387
    Publication Pharmaceutical Statistics
    DOI 10.1002/pst.2387
    Journal Abbr Pharmaceutical Statistics
    ISSN 1539-1604, 1539-1612
    Date Added 4/2/2024, 7:37:04 AM
    Modified 4/2/2024, 7:37:40 AM

    Tags:

    • bayes
    • prior-distributions
    • adaptive
    • adaptive-clinical-trials
    • priors
  • Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy

    Item Type Journal Article
    Author Steven N. Goodman
    Date 1999-06
    URL http://dx.doi.org/10.7326/0003-4819-130-12-199906150-00008
    Extra Citation Key: goo99tow tex.citeulike-article-id= 14434285 tex.citeulike-linkout-0= http://dx.doi.org/10.7326/0003-4819-130-12-199906150-00008 tex.day= 15 tex.posted-at= 2017-09-19 15:46:02 tex.priority= 0
    Volume 130
    Pages 995+
    Publication Ann Int Med
    DOI 10.7326/0003-4819-130-12-199906150-00008
    Issue 12
    ISSN 0003-4819
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • misinterpretation-of-p-values
    • p-values

    Notes:

    • Nice language for what happens when scientists use NHST to justify strong statements in their conclusions and interpretation; p-value fallacy

  • A comment on replication, P-values and evidence

    Item Type Journal Article
    Author Steven N. Goodman
    Date 1998
    Extra Citation Key: goo92com tex.citeulike-article-id= 13264159 tex.posted-at= 2014-07-14 14:09:30 tex.priority= 0
    Volume 11
    Pages 875-879
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • problems-in-interpreting-p-value-as-an-observed-error-rate
    • replication-probability
  • League tables and their limitations: Statistical issues in comparisons of institutional performance

    Item Type Journal Article
    Author Harvey Goldstein
    Author David J. Spiegelhalter
    Date 1996
    Extra Citation Key: gol96lea tex.citeulike-article-id= 13264154 tex.posted-at= 2014-07-14 14:09:30 tex.priority= 0
    Volume 159
    Pages 385-443
    Publication J Roy Stat Soc A
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • hierarchical-models
    • bayesian-methods
    • shrinkage
    • multilevel-models
    • physician-profiling
    • ranking-outcomes-and-institutions
  • Estimating design operating characteristics in Bayesian adaptive clinical trials

    Item Type Journal Article
    Author Shirin Golchi
    Abstract Bayesian adaptive designs have gained popularity in all phases of clinical trials with numerous new developments in the past few decades. During the COVID-19 pandemic, the need to establish evidence for the effectiveness of vaccines, therapeutic treatments, and policies that could resolve or control the crisis emphasized the advantages offered by efficient and flexible clinical trial designs. In many COVID-19 clinical trials, because of the high level of uncertainty, Bayesian adaptive designs were considered advantageous. Designing Bayesian adaptive trials, however, requires extensive simulation studies that are generally considered challenging, particularly in time-sensitive settings such as a pandemic. In this article, we propose a set of methods for efficient estimation and uncertainty quantification for design operating characteristics of Bayesian adaptive trials. Specifically, we model the sampling distribution of Bayesian probability statements that are commonly used as the basis of decision making. To showcase the implementation and performance of the proposed approach, we use a clinical trial design with an ordinal disease-progression scale endpoint that was popular among COVID-19 trials. However, the proposed methodology may be applied generally in the clinical trial context where design operating characteristics cannot be obtained analytically.
    Date 2022
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/cjs.11699
    Accessed 4/17/2022, 7:25:54 AM
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjs.11699
    Volume n/a
    Publication Canadian Journal of Statistics
    DOI 10.1002/cjs.11699
    Issue n/a
    ISSN 1708-945X
    Date Added 4/17/2022, 7:25:54 AM
    Modified 4/17/2022, 7:26:40 AM

    Tags:

    • bayes
    • rct
    • adaptive
    • experimental-design
    • optimal-design
  • Strictly proper scoring rules, prediction, and estimation

    Item Type Journal Article
    Author Tilmann Gneiting
    Author Adrian E. Raftery
    Date 2007
    Extra Citation Key: gne07str tex.citeulike-article-id= 13265560 tex.posted-at= 2014-07-14 14:09:59 tex.priority= 0
    Volume 102
    Pages 359-378
    Publication J Am Stat Assoc
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • cross-validation
    • bayes-factor
    • prediction
    • skill-score
    • proper-scoring-rule
    • accuracy
    • predictive-distribution
    • prediction-interval
    • brier-score
    • coherent
    • continuous-ranked-probability-score
    • entropy
    • kernel-score
    • loss-function
    • minimum-contrast-estimation
    • negative-definite-function
    • quantile-forecast
    • strictly-proper
    • utility-function

    Notes:

    • wonderful review article except missing references from Scandanavian and German medical decision making literature

  • Publication bias in meta-analysis: A Bayesian data-augmentation approach to account for issues exemplified in the passive smoking debate (with discussion)

    Item Type Journal Article
    Author Geof H. Givens
    Author D. D. Smith
    Author R. L. Tweedie
    Date 1997
    Extra Citation Key: giv97pub tex.citeulike-article-id= 13264137 tex.posted-at= 2014-07-14 14:09:29 tex.priority= 0
    Volume 12
    Pages 221-250
    Publication Stat Sci
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • data-augmentation
    • publication-bias
    • meta-analysis
  • A language and program for complex Bayesian modeling

    Item Type Journal Article
    Author W. R. Gilks
    Author A. Thomas
    Author D. J. Spiegelhalter
    Date 1994
    URL http://www.mrc-bsu.cam.ac.uk/bugs
    Extra Citation Key: bugs2 tex.citeulike-article-id= 13263828 tex.citeulike-linkout-0= http://www.mrc-bsu.cam.ac.uk/bugs tex.posted-at= 2014-07-14 14:09:23 tex.priority= 0
    Volume 43
    Pages 169-177
    Publication The Statistician
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
  • Stopping a trial early: Frequentist and Bayesian approaches applied to a CALGB trial of non-small cell lung cancer

    Item Type Journal Article
    Author S. L. George
    Author C. Li
    Author D. A. Berry
    Author M. R. Green
    Date 1994
    URL http://dx.doi.org/10.1002/sim.4780131305
    Extra Citation Key: geo94sto tex.citeulike-article-id= 13264131 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.4780131305 tex.posted-at= 2014-07-14 14:09:29 tex.priority= 0
    Volume 13
    Pages 1313-1328
    Publication Stat Med
    DOI 10.1002/sim.4780131305
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • posterior
    • updating

    Notes:

    • nice graph showing updating of posterior

  • R-squared for Bayesian Regression Models

    Item Type Journal Article
    Author Andrew Gelman
    Author Ben Goodrich
    Author Jonah Gabry
    Author Aki Vehtari
    Abstract The usual definition of R2 (variance of the predicted values divided by the variance of the data) has a problem for Bayesian fits, as the numerator can be larger than the denominator. We propose an alternative definition similar to one that has appeared in the survival analysis literature: the variance of the predicted values divided by the variance of predicted values plus the expected variance of the errors.
    Date December 10, 2018
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/00031305.2018.1549100
    Accessed 5/17/2019, 8:42:28 AM
    Volume 0
    Pages 1-7
    Publication The American Statistician
    DOI 10.1080/00031305.2018.1549100
    Issue 0
    ISSN 0003-1305
    Date Added 5/17/2019, 8:42:28 AM
    Modified 5/17/2019, 8:43:19 AM

    Tags:

    • bayes
    • predictive-accuracy
    • regression
    • accuracy
  • Bayesian Workflow

    Item Type Journal Article
    Author Andrew Gelman
    Author Aki Vehtari
    Author Daniel Simpson
    Author Charles C. Margossian
    Author Bob Carpenter
    Author Yuling Yao
    Author Lauren Kennedy
    Author Jonah Gabry
    Author Paul-Christian Bürkner
    Author Martin Modrák
    Abstract The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilistic programming languages make it easier to specify and fit Bayesian models, but this still leaves us with many options regarding constructing, evaluating, and using these models, along with many remaining challenges in computation. Using Bayesian inference to solve real-world problems requires not only statistical skills, subject matter knowledge, and programming, but also awareness of the decisions made in the process of data analysis. All of these aspects can be understood as part of a tangled workflow of applied Bayesian statistics. Beyond inference, the workflow also includes iterative model building, model checking, validation and troubleshooting of computational problems, model understanding, and model comparison. We review all these aspects of workflow in the context of several examples, keeping in mind that in practice we will be fitting many models for any given problem, even if only a subset of them will ultimately be relevant for our conclusions.
    Date 2020-11-03
    Library Catalog arXiv.org
    URL http://arxiv.org/abs/2011.01808
    Accessed 9/8/2021, 12:13:48 PM
    Extra arXiv: 2011.01808
    Publication arXiv:2011.01808 [stat]
    Date Added 9/8/2021, 12:13:48 PM
    Modified 9/8/2021, 12:14:06 PM

    Tags:

    • bayes
    • strategy
    • computational

    Notes:

    • Comment: 77 pages, 35 figures

  • Beyond subjective and objective in statistics

    Item Type Manuscript
    Author Andrew Gelman
    Author Christian Hennig
    Date 2017-06
    URL http://www.stat.columbia.edu/̃gelman/research/published/objectivityr5.pdf
    Extra Citation Key: gel17bey tex.citeulike-article-id= 14389022 tex.day= 21 tex.posted-at= 2017-07-06 21:31:21 tex.priority= 2
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • objectivity
  • Bayesian and Frequentist Regression Methods

    Item Type Journal Article
    Author Andrew Gelman
    Date 2015-03
    URL http://dx.doi.org/10.1002/sim.6427
    Extra Citation Key: gel15bay tex.citeulike-article-id= 14187479 tex.citeulike-attachment-1= Gelman-2015-Statistics<sub>i</sub>n<sub>M</sub>edicine.pdf; /pdf/user/harrelfe/article/14187479/1092189/Gelman-2015-Statistics<sub>i</sub>n<sub>M</sub>edicine.pdf; f721f578779a78858951150d3d213e55da97c22d tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.6427 tex.day= 30 tex.posted-at= 2016-11-20 13:39:31 tex.priority= 0
    Volume 34
    Pages 1259-1260
    Publication Stat Med
    DOI 10.1002/sim.6427
    Issue 7
    ISSN 02776715
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
  • P Values and Statistical Practice

    Item Type Journal Article
    Author Andrew Gelman
    Date 2013-01
    URL http://dx.doi.org/10.1097/ede.0b013e31827886f7
    Extra Citation Key: gel13pva tex.citeulike-article-id= 12016982 tex.citeulike-attachment-1= gel13pva.pdf; /pdf/user/harrelfe/article/12016982/1093670/gel13pva.pdf; 427d0fe50a4a72ea4942faafd733b289079aba1f tex.citeulike-linkout-0= http://dx.doi.org/10.1097/ede.0b013e31827886f7 tex.citeulike-linkout-1= http://view.ncbi.nlm.nih.gov/pubmed/23232612 tex.citeulike-linkout-2= http://www.hubmed.org/display.cgi?uids=23232612 tex.pmid= 23232612 tex.posted-at= 2016-12-05 15:48:53 tex.priority= 0
    Volume 24
    Pages 69-72
    Publication Epi
    DOI 10.1097/ede.0b013e31827886f7
    Issue 1
    ISSN 1044-3983
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • choice-of-prior
    • misinterpretation-of-p-values
    • p-values
  • Statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation

    Item Type Journal Article
    Author Margaret Gamalo-Siebers
    Author Jasmina Savic
    Author Cynthia Basu
    Author Xin Zhao
    Author Mathangi Gopalakrishnan
    Author Aijun Gao
    Author Guochen Song
    Author Simin Baygani
    Author Laura Thompson
    Author H. Amy Xia
    Author Karen Price
    Author Ram Tiwari
    Author Bradley P. Carlin
    Date 2017
    URL http://dx.doi.org/10.1002/pst.1807
    Extra Citation Key: gam17sta tex.citeulike-article-id= 14346294 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/pst.1807 tex.posted-at= 2017-04-28 14:51:20 tex.priority= 2
    Publication Pharm Stat
    DOI 10.1002/pst.1807
    ISSN 15391604
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • choice-of-prior
    • drug-development
    • pediatric
  • The what, why and how of Bayesian clinical trials monitoring

    Item Type Journal Article
    Author Laurence S. Freedman
    Author David J. Spiegelhalter
    Author Mahesh K. B. Parmar
    Date 1994
    Extra Citation Key: fre94wha tex.citeulike-article-id= 13264101 tex.posted-at= 2014-07-14 14:09:29 tex.priority= 0
    Volume 13
    Pages 1371-1383
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • study-design
    • early-termination
    • sequential-monitoring

    Notes:

    • gives example adjustment for sequential testing yields 0.95 CI that includes 0.0 even for data indicating that study should be stopped at the first interim analysis

  • Bayesian statistical methods

    Item Type Journal Article
    Author Laurence Freedman
    Date 1996
    Extra Citation Key: fre96bay tex.citeulike-article-id= 13264103 tex.posted-at= 2014-07-14 14:09:29 tex.priority= 0
    Volume 313
    Pages 569-570
    Publication BMJ
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • teaching-mds
  • Coronary sinus reducer for the treatment of refractory angina (ORBITA-COSMIC): a randomised, placebo-controlled trial

    Item Type Journal Article
    Author Michael J Foley
    Author Christopher A Rajkumar
    Author Fiyyaz Ahmed-Jushuf
    Author Florentina A Simader
    Author Shayna Chotai
    Author Rachel H Pathimagaraj
    Author Muhammad Mohsin
    Author Ahmed Salih
    Author Danqi Wang
    Author Prithvi Dixit
    Author John R Davies
    Author Tom R Keeble
    Author Claudia Cosgrove
    Author James C Spratt
    Author Peter D O’Kane
    Author Ranil De Silva
    Author Jonathan M Hill
    Author Sukhjinder S Nijjer
    Author Sayan Sen
    Author Ricardo Petraco
    Author Ghada W Mikhail
    Author Ramzi Khamis
    Author Tushar Kotecha
    Author Frank E Harrell
    Author Peter Kellman
    Author Darrel P Francis
    Author James P Howard
    Author Graham D Cole
    Author Matthew J Shun-Shin
    Author Rasha K Al-Lamee
    Date 4/2024
    Language en
    Short Title Coronary sinus reducer for the treatment of refractory angina (ORBITA-COSMIC)
    Library Catalog DOI.org (Crossref)
    URL https://linkinghub.elsevier.com/retrieve/pii/S0140673624002563
    Accessed 4/11/2024, 7:44:51 AM
    Pages S0140673624002563
    Publication The Lancet
    DOI 10.1016/S0140-6736(24)00256-3
    Journal Abbr The Lancet
    ISSN 01406736
    Date Added 4/11/2024, 7:44:51 AM
    Modified 4/11/2024, 7:47:29 AM

    Tags:

    • longitudinal
    • ordinal
    • bayes
    • rct
    • markov
    • rmsb
  • Comments on Bayesian and frequentist analysis and interpretation of clinical trials

    Item Type Journal Article
    Author Lloyd D. Fisher
    Date 1996
    Extra Citation Key: fis96com tex.citeulike-article-id= 13264077 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0
    Volume 17
    Pages 423-434
    Publication Controlled Clin Trials
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • randomized-trials
    • problems-selecting-priors
    • stylized-priors
  • Tutorial in Biostatistics: Bayesian data monitoring in clinical trials

    Item Type Journal Article
    Author Peter M. Fayers
    Author Deborah Ashby
    Author Mahesh K. Parmar
    Date 1997
    Extra Citation Key: fay97bay tex.citeulike-article-id= 13264065 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0
    Volume 16
    Pages 1413-1430
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • bayesian-inference
    • study-design
    • choice-of-prior-distribution
    • sequential-monitoring
    • convincing-clinicians-to-alter-medical-practice
    • skeptical-prior
    • teaching-paper
  • The current state of Bayesian methods in nonclinical pharmaceutical statistics: Survey results and recommendations from the DIA/ASA-BIOP Nonclinical Bayesian Working Group

    Item Type Journal Article
    Author Paul Faya
    Author Perceval Sondag
    Author Steven Novick
    Author Dwaine Banton
    Author John W. Seaman Jr
    Author James D. Stamey
    Author Bruno Boulanger
    Abstract The use of Bayesian methods to support pharmaceutical product development has grown in recent years. In clinical statistics, the drive to provide faster access for patients to medical treatments has led to a heightened focus by industry and regulatory authorities on innovative clinical trial designs, including those that apply Bayesian methods. In nonclinical statistics, Bayesian applications have also made advances. However, they have been embraced far more slowly in the nonclinical area than in the clinical counterpart. In this article, we explore some of the reasons for this slower rate of adoption. We also present the results of a survey conducted for the purpose of understanding the current state of Bayesian application in nonclinical areas and for identifying areas of priority for the DIA/ASA-BIOP Nonclinical Bayesian Working Group. The survey explored current usage, hurdles, perceptions, and training needs for Bayesian methods among nonclinical statisticians. Based on the survey results, a set of recommendations is provided to help guide the future advancement of Bayesian applications in nonclinical pharmaceutical statistics.
    Date 2021
    Language en
    Short Title The current state of Bayesian methods in nonclinical pharmaceutical statistics
    Library Catalog Wiley Online Library
    URL http://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2072
    Accessed 3/7/2021, 9:40:41 AM
    Rights © 2020 John Wiley & Sons Ltd
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2072
    Volume 20
    Pages 245-255
    Publication Pharmaceutical Statistics
    DOI https://doi.org/10.1002/pst.2072
    Issue 2
    ISSN 1539-1612
    Date Added 3/7/2021, 9:40:41 AM
    Modified 3/7/2021, 9:41:31 AM

    Tags:

    • bayes
    • drug-development
    • adoption
  • Large sample Bayesian inference on the parameters of the proportional hazard model

    Item Type Journal Article
    Author David Faraggi
    Author Richard Simon
    Date 1997
    Extra Citation Key: far97lar tex.citeulike-article-id= 13264064 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0
    Volume 16
    Pages 2573-2585
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • variable-selection
    • approximate-bayesian-inference
  • To amnio or not to amnio: That is the decision for Bayes

    Item Type Journal Article
    Author Juanjuan Fan
    Author Richard A. Levine
    Date 2007
    Extra Citation Key: fan07amn tex.citeulike-article-id= 13265622 tex.posted-at= 2014-07-14 14:10:00 tex.priority= 0
    Volume 20
    Pages 26-32
    Publication Chance
    Issue 3
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • diagnosis
    • decision-theory
    • bayes-decision-tutorial
    • utility-theory
  • Bayesian statistical methods in public health and medicine

    Item Type Journal Article
    Author R. D. Etzioni
    Author J. B. Kadane
    Date 1995
    Extra Citation Key: etz95bay tex.citeulike-article-id= 13264055 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0
    Volume 16
    Pages 23-41
    Publication Ann Rev Pub Hlth
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • teaching
  • Dealing with missing covariates in epidemiologic studies: a comparison between multiple imputation and a full Bayesian approach

    Item Type Journal Article
    Author Nicole S. Erler
    Author Dimitris Rizopoulos
    Author Joost Rosmalen
    Author Vincent W. V. Jaddoe
    Author Oscar H. Franco
    Author Emmanuel M. E. H. Lesaffre
    Date 2016-07
    URL http://dx.doi.org/10.1002/sim.6944
    Extra Citation Key: erl16dea tex.citeulike-article-id= 14240448 tex.citeulike-attachment-1= erl16dea.pdf; /pdf/user/harrelfe/article/14240448/1096087/erl16dea.pdf; fd801a70c09a641a0f364c5d5ec25a273f4dc27e tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.6944 tex.day= 30 tex.posted-at= 2016-12-29 15:43:06 tex.priority= 0
    Volume 35
    Pages 2955-2974
    Publication Stat Med
    DOI 10.1002/sim.6944
    Issue 17
    ISSN 02776715
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • bayesian-methods
    • missing-data
  • Missing data: An update on the state of the art.

    Item Type Journal Article
    Author Craig K. Enders
    Date 2023-03-16
    Language en
    Short Title Missing data
    Library Catalog DOI.org (Crossref)
    URL https://doi.apa.org/doi/10.1037/met0000563
    Accessed 4/29/2024, 3:28:37 PM
    Rights http://www.apa.org/pubs/journals/resources/open-access.aspx
    Publication Psychological Methods
    DOI 10.1037/met0000563
    Journal Abbr Psychological Methods
    ISSN 1939-1463, 1082-989X
    Date Added 4/29/2024, 3:28:37 PM
    Modified 4/29/2024, 3:29:11 PM

    Tags:

    • bayes
    • imputation
    • review
    • missing
  • Frequentist evaluation of group sequential clinical trial designs

    Item Type Journal Article
    Author Scott S. Emerson
    Author John M. Kittelson
    Author Daniel L. Gillen
    Date 2007
    URL http://dx.doi.org/10.1002/sim.2901
    Extra Citation Key: eme07fre tex.citeulike-article-id= 13265647 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.2901 tex.posted-at= 2014-07-14 14:10:01 tex.priority= 0
    Volume 26
    Pages 5047-5080
    Publication Stat Med
    DOI 10.1002/sim.2901
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • sample-size
    • tutorial
    • interim-analyses
    • sequential-monitoring
    • monitoring
    • group-sequential-test
    • operating-characteristics
    • stopping-rules
  • Stopping a clinical trial very early based on unplanned interim analysis: A group sequential approach

    Item Type Journal Article
    Author Scott S. Emerson
    Date 1995
    Extra Citation Key: eme95sto tex.citeulike-article-id= 13264046 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0
    Volume 51
    Pages 1152-1162
    Publication Biometrics
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • study-design
    • group-sequential-monitoring
    • monitoring
    • unplanned-analysis
    • why-bayesian
  • Are people Bayesian? Uncovering behavioral strategies

    Item Type Journal Article
    Author Mahmoud A. El-Gamal
    Author David M. Grether
    Date 1995
    Extra Citation Key: elg95peo tex.citeulike-article-id= 13264045 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0
    Volume 90
    Pages 1137-1145
    Publication J Am Stat Assoc
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • predictive-accuracy
    • bayes-rule
    • probability-assessment
  • Data analysis using Stein's estimator and its generalizations

    Item Type Journal Article
    Author Bradley Efron
    Author Carl Morris
    Date 1975
    Extra Citation Key: efr75dat tex.citeulike-article-id= 13264031 tex.posted-at= 2014-07-14 14:09:27 tex.priority= 0
    Volume 70
    Pages 311-319
    Publication J Am Stat Assoc
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • shrinkage
    • empirical-bayes
    • james-stein
  • Bayes and likelihood calculations from confidence intervals

    Item Type Journal Article
    Author Bradley Efron
    Date 1993
    Extra Citation Key: efr93bay tex.citeulike-article-id= 13264041 tex.posted-at= 2014-07-14 14:09:27 tex.priority= 0
    Volume 80
    Pages 3-26
    Publication Biometrika
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • bayesian-intervals-and-confidence-intervals
  • Bayesian statistical inference for psychological research

    Item Type Journal Article
    Author Ward Edwards
    Author Harold Lindman
    Author Leonard J. Savage
    Abstract Bayesian statistics, a currently controversial viewpoint concerning statistical inference, is based on a definition of probability as a particular measure of the opinions of ideally consistent people. Statistical inference is modification of these opinions in the light of evidence, and Bayes' theorem specifies how such modifications should be made. The tools of Bayesian statistics include the theory of specific distributions and the principle of stable estimation, which specifies when actual prior opinions may be satisfactorily approximated by a uniform distribution. A common feature of many classical significance tests is that a sharp null hypothesis is compared with a diffuse alternative hypothesis. Often evidence which, for a Bayesian statistician, strikingly supports the null hypothesis leads to rejection of that hypothesis by standard classical procedures. The likelihood principle emphasized in Bayesian statistics implies, among other things, that the rules governing when data collection stops are irrelevant to data interpretation. It is entirely appropriate to collect data until a point has been proven or disproven, or until the data collector runs out of time, money, or patience.
    Date 1963-05
    URL http://psycnet.apa.org/doi/10.1037/h0044139
    Extra Citation Key: edw63bay tex.citeulike-article-id= 14287855 tex.citeulike-linkout-0= http://psycnet.apa.org/doi/10.1037/h0044139 tex.posted-at= 2017-02-26 17:54:58 tex.priority= 2
    Volume 70
    Pages 193-242
    Publication Psych Rev
    Issue 3
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
  • A flexible multi‐metric Bayesian framework for decision‐making in Phase II multi‐arm multi‐stage studies

    Item Type Journal Article
    Author Suzanne M. Dufault
    Author Angela M. Crook
    Author Katie Rolfe
    Author Patrick P. J. Phillips
    Abstract We propose a multi‐metric flexible Bayesian framework to support efficient interim decision‐making in multi‐arm multi‐stage phase II clinical trials. Multi‐arm multi‐stage phase II studies increase the efficiency of drug development, but early decisions regarding the futility or desirability of a given arm carry considerable risk since sample sizes are often low and follow‐up periods may be short. Further, since intermediate outcomes based on biomarkers of treatment response are rarely perfect surrogates for the primary outcome and different trial stakeholders may have different levels of risk tolerance, a single hypothesis test is insufficient for comprehensively summarizing the state of the collected evidence. We present a Bayesian framework comprised of multiple metrics based on point estimates, uncertainty, and evidence towards desired thresholds (a Target Product Profile) for (1) ranking of arms and (2) comparison of each arm against an internal control. Using a large public‐private partnership targeting novel TB arms as a motivating example, we find via simulation study that our multi‐metric framework provides sufficient confidence for decision‐making with sample sizes as low as 30 patients per arm, even when intermediate outcomes have only moderate correlation with the primary outcome. Our reframing of trial design and the decision‐making procedure has been well‐received by research partners and is a practical approach to more efficient assessment of novel therapeutics.
    Date 12/2023
    Language en
    Library Catalog DOI.org (Crossref)
    URL https://onlinelibrary.wiley.com/doi/10.1002/sim.9961
    Accessed 12/9/2023, 11:01:39 AM
    Pages sim.9961
    Publication Statistics in Medicine
    DOI 10.1002/sim.9961
    Journal Abbr Statistics in Medicine
    ISSN 0277-6715, 1097-0258
    Date Added 12/9/2023, 11:01:39 AM
    Modified 12/9/2023, 11:02:00 AM

    Tags:

    • bayes
    • rct
    • multiple-events
  • Bayesian subset analysis in a colorecta cancer clinical trial

    Item Type Journal Article
    Author D. O. Dixon
    Author R. Simon
    Date 1992
    Extra Citation Key: dix92bay tex.citeulike-article-id= 13264005 tex.posted-at= 2014-07-14 14:09:27 tex.priority= 0
    Volume 11
    Pages 13-22
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • bayesian-inference
    • subgroup-analysis
    • shrinkage
  • Bayesian subset analysis

    Item Type Journal Article
    Author D. O. Dixon
    Author R. Simon
    Date 1991
    Extra Citation Key: dix91bay tex.citeulike-article-id= 13264004 tex.posted-at= 2014-07-14 14:09:27 tex.priority= 0
    Volume 47
    Pages 871-881
    Publication Biometrics
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • subgroup-analysis
    • shrinkage
  • Meta-analysis in clinical trials

    Item Type Journal Article
    Author R. DerSimonian
    Author N. Laird
    Date 1986
    Extra Citation Key: der86met tex.citeulike-article-id= 13263991 tex.posted-at= 2014-07-14 14:09:27 tex.priority= 0
    Volume 7
    Pages 177-188
    Publication Controlled Clin Trials
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • meta-analysis
    • random-effects-model
    • empirical-bayes
  • Improving transparency and replication in Bayesian statistics: The WAMBS-Checklist

    Item Type Journal Article
    Author Sarah Depaoli
    Author Rens van de Schoot
    Abstract Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research. Although it is very attractive to use Bayesian statistics, our personal experience has led us to believe that naively applying Bayesian methods can be dangerous for at least 3 main reasons: the potential influence of priors, misinterpretation of Bayesian features and results, and improper reporting of Bayesian results. To deal with these 3 points of potential danger, we have developed a succinct checklist: the WAMBS-checklist (When to worry and how to Avoid the Misuse of Bayesian Statistics). The purpose of the questionnaire is to describe 10 main points that should be thoroughly checked when applying Bayesian analysis. We provide an account of "when to worry" for each of these issues related to: (a) issues to check before estimating the model, (b) issues to check after estimating the model but before interpreting results, (c) understanding the influence of priors, and (d) actions to take after interpreting results. To accompany these key points of concern, we will present diagnostic tools that can be used in conjunction with the development and assessment of a Bayesian model. We also include examples of how to interpret results when "problems" in estimation arise, as well as syntax and instructions for implementation. Our aim is to stress the importance of openness and transparency of all aspects of Bayesian estimation, and it is our hope that the WAMBS questionnaire can aid in this process. (PsycINFO Database Record
    Date 2017-06
    Language eng
    Short Title Improving transparency and replication in Bayesian statistics
    Library Catalog PubMed
    Extra PMID: 26690773
    Volume 22
    Pages 240-261
    Publication Psychological Methods
    DOI 10.1037/met0000065
    Issue 2
    Journal Abbr Psychol Methods
    ISSN 1939-1463
    Date Added 1/21/2021, 5:16:22 PM
    Modified 1/21/2021, 5:17:12 PM

    Tags:

    • bayes
    • reporting
    • basic
    • diagnostics

    Attachments

    • PubMed entry
  • A Bayesian CART algorithm

    Item Type Journal Article
    Author David G. T. Denison
    Author Bani K. Mallick
    Author Adrian F. M. Smith
    Date 1998
    Extra Citation Key: den98bay tex.citeulike-article-id= 13263990 tex.posted-at= 2014-07-14 14:09:27 tex.priority= 0
    Volume 85
    Pages 363-377
    Publication Biometrika
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • cart
    • recursive-partitioning
    • bayesian-modeling
    • choosing-most-probable-tree
    • reversible-jump-mcmc
  • Joint control of consensus and evidence in Bayesian design of clinical trials

    Item Type Journal Article
    Author Fulvio De Santis
    Author Stefania Gubbiotti
    Abstract In Bayesian inference, prior distributions formalize preexperimental information and uncertainty on model parameters. Sometimes different sources of knowledge are available, possibly leading to divergent posterior distributions and inferences. Research has been recently devoted to the development of sample size criteria that guarantee agreement of posterior information in terms of credible intervals when multiple priors are available. In these articles, the goals of reaching consensus and evidence are typically kept separated. Adopting a Bayesian performance-based approach, the present article proposes new sample size criteria for superiority trials that jointly control the achievement of both minimal evidence and consensus, measured by appropriate functions of the posterior distributions. We develop both an average criterion and a more stringent criterion that accounts for the entire predictive distributions of the selected measures of minimal evidence and consensus. Methods are developed and illustrated via simulation for trials involving binary outcomes. A real clinical trial example on Covid-19 vaccine data is presented.
    Date 2021
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.202100035
    Accessed 12/11/2021, 3:32:01 PM
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202100035
    Volume n/a
    Publication Biometrical Journal
    DOI 10.1002/bimj.202100035
    Issue n/a
    ISSN 1521-4036
    Date Added 12/11/2021, 3:32:01 PM
    Modified 12/12/2021, 2:42:29 PM

    Tags:

    • bayes
    • sample-size
    • teaching
    • prior
    • prior-elicitation
  • Why optional stopping can be a problem for Bayesians

    Item Type Journal Article
    Author Rianne de Heide
    Author Peter D. Grünwald
    Abstract Recently, optional stopping has been a subject of debate in the Bayesian psychology community. Rouder (Psychonomic Bulletin & Review 21(2), 301–308, 2014) argues that optional stopping is no problem for Bayesians, and even recommends the use of optional stopping in practice, as do (Wagenmakers, Wetzels, Borsboom, van der Maas & Kievit, Perspectives on Psychological Science 7, 627–633, 2012). This article addresses the question of whether optional stopping is problematic for Bayesian methods, and specifies under which circumstances and in which sense it is and is not. By slightly varying and extending Rouder’s (Psychonomic Bulletin & Review 21(2), 301–308, 2014) experiments, we illustrate that, as soon as the parameters of interest are equipped with default or pragmatic priors—which means, in most practical applications of Bayes factor hypothesis testing—resilience to optional stopping can break down. We distinguish between three types of default priors, each having their own specific issues with optional stopping, ranging from no-problem-at-all (type 0 priors) to quite severe (type II priors).
    Date 2021-06-01
    Language en
    Library Catalog Springer Link
    URL https://doi.org/10.3758/s13423-020-01803-x
    Accessed 3/24/2022, 11:11:58 AM
    Volume 28
    Pages 795-812
    Publication Psychonomic Bulletin & Review
    DOI 10.3758/s13423-020-01803-x
    Issue 3
    Journal Abbr Psychon Bull Rev
    ISSN 1531-5320
    Date Added 3/24/2022, 11:12:03 AM
    Modified 3/24/2022, 11:12:56 AM

    Tags:

    • bayes
    • prior
    • bayes-factor
    • sequential-monitoring
    • sequential
    • stopping

    Notes:

    • Interesting taxonomy of priors

  • Comment on “the philosophy of statistics” by D. V. Lindley

    Item Type Journal Article
    Author A. P. Dawid
    Date 2000
    Extra Citation Key: daw00com tex.citeulike-article-id= 14438464 tex.posted-at= 2017-09-26 18:54:59 tex.priority= 2
    Volume 49
    Pages 325-326
    Publication The Statistician
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
  • Better decision making in drug development through adoption of formal prior elicitation

    Item Type Journal Article
    Author Nigel Dallow
    Author Nicky Best
    Author Timothy H. Montague
    Abstract With the continued increase in the use of Bayesian methods in drug development, there is a need for statisticians to have tools to develop robust and defensible informative prior distributions. Whilst relevant empirical data should, where possible, provide the basis for such priors, it is often the case that limitations in data and/or our understanding may preclude direct construction of a data‐based prior. Formal expert elicitation methods are a key technique that can be used to determine priors in these situations. Within GlaxoSmithKline, we have adopted a structured approach to prior elicitation on the basis of the SHELF elicitation framework and routinely use this in conjunction with calculation of probability of success (assurance) of the next study(s) to inform internal decision making at key project milestones. The aim of this paper is to share our experiences of embedding the use of prior elicitation within a large pharmaceutical company, highlighting both the benefits and challenges of prior elicitation through a series of case studies. We have found that putting team beliefs into the shape of a quantitative probability distribution provides a firm anchor for all internal decision making, enabling teams to provide investment boards with formally appropriate estimates of the probability of trial success as well as robust plans for interim decision rules where appropriate. As an added benefit, the elicitation process provides transparency about the beliefs and risks of the potential medicine, ultimately enabling better portfolio and company‐wide decision making.
    Date 2018
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1854
    Extra Citation Key: dal18bet tex.citeulike-article-id= 14560064 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/pst.1854 tex.citeulike-linkout-1= https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1854 tex.eprint= https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.1854 tex.posted-at= 2018-04-03 03:40:59 tex.priority= 2
    Volume 0
    Publication Pharm Stat
    DOI 10.1002/pst.1854
    Issue 0
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • prior
    • choice-of-prior
    • clinical-decision-making
    • drug-development
    • elicitation
  • The Case for a Bayesian Approach to Benefit-Risk Assessment:

    Item Type Journal Article
    Author Maria J. Costa
    Author Weili He
    Author Yannis Jemiai
    Author Yueqin Zhao
    Author Carl Di Casoli
    Date 2017-04
    URL http://dx.doi.org/10.1177/2168479017698190
    Extra Citation Key: cos17cas tex.citeulike-article-id= 14476450 tex.citeulike-attachment-1= cos17cas.pdf; /pdf/user/harrelfe/article/14476450/1122771/cos17cas.pdf; 0ee7b8048869b3a12607d86e707e3e107426ffa5 tex.citeulike-linkout-0= http://dx.doi.org/10.1177/2168479017698190 tex.day= 04 tex.posted-at= 2017-11-15 12:11:34 tex.priority= 0
    Volume 51
    Pages 568-574
    Publication Therapeutic Innovation & Regulatory Science
    DOI 10.1177/2168479017698190
    Issue 5
    ISSN 2168-4790
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • bayesian-methods
    • pharmaceutical-safety
    • drug-development
    • risk-benefit-ratio
  • Bayesian joint modelling of benefit and risk in drug development

    Item Type Journal Article
    Author Maria J. Costa
    Author Thomas Drury
    Date 2018-02
    URL http://dx.doi.org/10.1002/pst.1852
    Extra Citation Key: cos18bay tex.citeulike-article-id= 14548318 tex.citeulike-attachment-1= cos18bay.pdf; /pdf/user/harrelfe/article/14548318/1131793/cos18bay.pdf; 30b53d12eaf2eda1a1fb76fcd4770fdd3f1d2662 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/pst.1852 tex.day= 22 tex.posted-at= 2018-03-13 19:54:23 tex.priority= 0
    Publication Pharm Stat
    DOI 10.1002/pst.1852
    ISSN 15391604
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • multiple-endpoints
    • bayesian-inference
    • drug-development
    • risk-benefit-ratio
  • Semi-parametric modelling for costs of health care technologies

    Item Type Journal Article
    Author C. Conigliani
    Author A. Tancredi
    Date 2005
    Extra Citation Key: con05sem tex.citeulike-article-id= 13265451 tex.posted-at= 2014-07-14 14:09:57 tex.priority= 0
    Volume 24
    Pages 3171-3184
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • analysis-of-cost
    • mcmc
    • cost-data
    • health-economics
    • extreme-value-theory
    • flexible-bayesian-model
    • generalized-pareto-distribution
  • Why are not There More Bayesian Clinical Trials? Perceived Barriers and Educational Preferences Among Medical Researchers Involved in Drug Development

    Item Type Journal Article
    Author Jennifer Clark
    Author Natalia Muhlemann
    Author Fanni Natanegara
    Author Andrew Hartley
    Author Deborah Wenkert
    Author Fei Wang
    Author Frank E. Harrell
    Author Ross Bray
    Author The Medical Outreach Subteam of the Drug Information Association Bayesian Scientific Working Group
    Abstract The clinical trials community has been hesitant to adopt Bayesian statistical methods, which are often more flexible and efficient with more naturally interpretable results than frequentist methods. We aimed to identify self-reported barriers to implementing Bayesian methods and preferences for becoming comfortable with them.
    Date 2022-01-03
    Language en
    Short Title Why are not There More Bayesian Clinical Trials?
    Library Catalog Springer Link
    URL https://doi.org/10.1007/s43441-021-00357-x
    Accessed 1/8/2022, 7:51:22 AM
    Publication Therapeutic Innovation & Regulatory Science
    DOI 10.1007/s43441-021-00357-x
    Journal Abbr Ther Innov Regul Sci
    ISSN 2168-4804
    Date Added 1/8/2022, 7:51:55 AM
    Modified 1/8/2022, 7:51:55 AM

    Tags:

    • bayes
    • teaching-mds
    • drug-development
  • Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas

    Item Type Journal Article
    Author Seoyoon Cho
    Author Matthew A Psioda
    Author Joseph G Ibrahim
    Abstract Abstract There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method’s value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial.
    Date 2024-04-26
    Language en
    Library Catalog DOI.org (Crossref)
    URL https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxae009/7658810
    Accessed 4/30/2024, 5:04:13 PM
    Rights https://academic.oup.com/pages/standard-publication-reuse-rights
    Pages kxae009
    Publication Biostatistics
    DOI 10.1093/biostatistics/kxae009
    ISSN 1465-4644, 1468-4357
    Date Added 4/30/2024, 5:04:13 PM
    Modified 4/30/2024, 5:04:33 PM

    Tags:

    • bayes
    • joint-model
    • copula
  • A Bayesian group sequential small n sequential multiple-assignment randomized trial

    Item Type Journal Article
    Author Yan-Cheng Chao
    Author Thomas M. Braun
    Author Roy N. Tamura
    Author Kelley M. Kidwell
    Abstract A small n, sequential, multiple-assignment, randomized trial (called ‘snSMART’) is a small sample multistage design where participants may be rerandomized to treatment on the basis of intermediate end points. This design is motivated by the ‘A randomized multicenter study for isolated skin vasculitis’ trial (NCT02939573): an on-going snSMART design focusing on the evaluation of three drugs for isolated skin vasculitis. By formulating an interim decision rule for removing one of the treatments, we use a Bayesian model and the resulting posterior distributions to provide sufficient evidence that one treatment is inferior to the other treatments before enrolling more participants. By doing so, we can remove the worst performing treatment at an interim analysis and prevent the subsequent participants from receiving the removed treatment. On the basis of simulation results, we have evidence that the treatment response rates can still be unbiasedly and efficiently estimated in our new design, especially for the treatments with higher response rates. In addition, by adjusting the decision rule criteria for the posterior probabilities, we can control the probability of incorrectly removing an effective treatment.
    Date 2020
    Language en
    Library Catalog Wiley Online Library
    URL https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssc.12406
    Accessed 4/12/2020, 11:23:48 AM
    Rights © 2020 Royal Statistical Society
    Extra _eprint: https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/rssc.12406
    Volume n/a
    Publication Journal of the Royal Statistical Society: Series C (Applied Statistics)
    DOI 10.1111/rssc.12406
    Issue n/a
    ISSN 1467-9876
    Date Added 4/12/2020, 11:23:48 AM
    Modified 4/12/2020, 11:24:44 AM

    Tags:

    • bayes
    • rct
    • adaptive
    • sequential
    • smart
  • Explaining the Gibbs sampler

    Item Type Journal Article
    Author George Casella
    Author Edward I. George
    Date 1992
    Extra Citation Key: cas92exp tex.citeulike-article-id= 13263865 tex.posted-at= 2014-07-14 14:09:24 tex.priority= 0
    Volume 46
    Pages 167-174
    Publication Am Statistician
    Date Added 7/7/2018, 1:38:33 PM
    Modified 1/19/2021, 8:04:49 AM

    Tags:

    • bayesian-inference
    • teaching
    • data-augmentation
    • resampling
    • gibbs-sampler
    • monte-carlo
  • Stan: A Probabilistic Programming Language

    Item Type Journal Article
    Author Bob Carpenter
    Author Andrew Gelman
    Author Matthew Hoffman
    Author Daniel Lee
    Author Ben Goodrich
    Author Michael Betancourt
    Author Marcus Brubaker
    Author Jiqiang Guo
    Author Peter Li
    Author Allen Riddell
    Abstract Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
    Date 2017
    URL https://www.jstatsoft.org/v076/i01
    Extra Citation Key: stan17 tex.citeulike-article-id= 14573584 tex.citeulike-linkout-0= http://dx.doi.org/10.18637/jss.v076.i01 tex.citeulike-linkout-1= https://www.jstatsoft.org/v076/i01 tex.posted-at= 2018-04-22 23:40:19 tex.priority= 2
    Volume 76
    Pages 1-32
    Publication J Stat Software
    DOI 10.18637/jss.v076.i01
    Issue 1
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • statistical-computing
    • bayesian-modeling
    • stan
  • Causal considerations can inform the interpretation of surprising associations in medical registries

    Item Type Journal Article
    Author Alberto Carmona-Bayonas
    Author Paula Jiménez-Fonseca
    Author Javier Gallego
    Author Pavlos Msaouel
    Abstract An exploratory analysis of registry data from 2437 patients with advanced gastric cancer revealed a surprising association between astrological birth sign and overall survival (OS) with p = 0.01. After dichotomizing or changing the reference sign, p-values <0.05 were observed for several birth signs following adjustments for multiple comparisons. Bayesian models with moderately skeptical priors still pointed to these associations. A more plausible causal model, justified by contextual knowledge, revealed that these associations arose from the astrological sign association with seasonality. This case study illustrates how causal considerations can guide analyses through what would otherwise be a hopeless maze of statistical possibilities.
    Date 2021-10-28
    Language eng
    Library Catalog PubMed
    Extra PMID: 34709109
    Pages 1-27
    Publication Cancer Investigation
    DOI 10.1080/07357907.2021.1999971
    Journal Abbr Cancer Invest
    ISSN 1532-4192
    Date Added 10/30/2021, 8:03:35 AM
    Modified 10/30/2021, 8:05:00 AM

    Tags:

    • bayes
    • causal-inference
    • causality
    • skeptical-prior
    • seasonality

    Attachments

    • PubMed entry
  • A remark on 'Bayesian predictive approach to interim monitoring in clinical trials' by A. Dmitrienko and M-D. Wang

    Item Type Journal Article
    Author Gengqian Cai
    Author Tianhui Zhou
    Date 2012
    URL http://dx.doi.org/10.1002/sim.4445
    Extra Citation Key: cai12rem tex.citeulike-article-id= 13265932 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.4445 tex.posted-at= 2014-07-14 14:10:07 tex.priority= 0
    Volume 31
    Pages 1774-1776
    Publication Stat Med
    DOI 10.1002/sim.4445
    Issue 16
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • interim-monitoring
    • monitoring
    • predictive-distribution

    Notes:

    • concise equation for normal approximation to Bayesian predictive probability

  • Helping doctors to draw appropriate inferences from the analysis of medical studies

    Item Type Journal Article
    Author Paul R. Burton
    Date 1994
    Extra Citation Key: bur94hel tex.citeulike-article-id= 13263835 tex.posted-at= 2014-07-14 14:09:24 tex.priority= 0
    Volume 1994
    Pages 1699-1713
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-methods
    • power
  • Ordinal Regression Models in Psychology: A Tutorial

    Item Type Journal Article
    Author Paul-Christian Bürkner
    Author Matti Vuorre
    Abstract Ordinal variables, while extremely common in Psychology, are almost exclusively analysed with statistical models that falsely assume them to be metric. This practice can lead to distorted effect size estimates, inflated error rates, and other problems. We argue for the application of ordinal models that make appropriate assumptions about the variables under study. In this tutorial article, we first explain the three major ordinal model classes; the cumulative, sequential and adjacent category models. We then show how to fit ordinal models in a fully Bayesian framework with the R package brms, using data sets on stem cell opinions and marriage time courses. Appendices provide detailed mathematical derivations of the models and a discussion of censored ordinal models. Ordinal models provide better theoretical interpretation and numerical inference from ordinal data, and we recommend their widespread adoption in Psychology.
    Date 2018-02-28T23:41:26.334Z
    Short Title Ordinal Regression Models in Psychology
    Library Catalog psyarxiv.com
    URL https://psyarxiv.com/x8swp/
    Accessed 1/7/2019, 1:52:26 PM
    DOI 10.31234/osf.io/x8swp
    Date Added 1/7/2019, 1:52:26 PM
    Modified 1/7/2019, 1:54:01 PM

    Tags:

    • ordinal
    • bayes
  • brms: An R Package for Bayesian Multilevel Models Using Stan

    Item Type Journal Article
    Author Paul-Christian Bürkner
    Date 2017
    URL http://dx.doi.org/10.18637/jss.v080.i01
    Extra Citation Key: brms tex.citeulike-article-id= 14573585 tex.citeulike-linkout-0= http://dx.doi.org/10.18637/jss.v080.i01 tex.posted-at= 2018-04-22 23:45:47 tex.priority= 2
    Volume 80
    Pages 1-28
    Publication Journal of Statistical Software
    DOI 10.18637/jss.v080.i01
    Issue 1
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • statistical-computing
    • bayesian-modeling
    • stan
  • Confidence intervals for multinomial logistic regression in sparse data

    Item Type Journal Article
    Author Shelley B. Bull
    Author Juan P. Lewinger
    Author Sophia S. F. Lee
    Date 2007
    Extra Citation Key: bul07con tex.citeulike-article-id= 13265558 tex.posted-at= 2014-07-14 14:09:59 tex.priority= 0
    Volume 26
    Pages 903-918
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bias-reduction
    • asymptotic-bias
    • bayesian-estimates

    Notes:

    • better than exact conditional methods when there are continuous covariates;data separation;infinite estimates;Jeffreys prior;odds ratio;polytomous logistic regression;small samples;penalization through the use of Jeffreys prior

  • Regression models for multiple outcomes in large epidemiologic studies

    Item Type Journal Article
    Author Shelley B. Bull
    Date 1998
    Extra Citation Key: bul98reg tex.citeulike-article-id= 13263831 tex.posted-at= 2014-07-14 14:09:24 tex.priority= 0
    Volume 17
    Pages 2179-2197
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • gee
    • shrinkage
    • multiple-outcomes
    • empirical-bayes
    • excellent-graphics
    • outcomes-research
    • common-covariable-effects-across-outcomes
  • Robust Bayesian sample size determination in clinical trials

    Item Type Journal Article
    Author Pierpaolo Brutti
    Author Fulvio De Santis
    Author Stefania Gubbiotti
    Date 2008
    Extra Citation Key: bru08rob tex.citeulike-article-id= 13265677 tex.posted-at= 2014-07-14 14:10:01 tex.priority= 0
    Volume 27
    Pages 2290-2306
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • analysis-and-design-priors
    • bayesian-power
    • bayesian-robustness
    • conditional-and-predictive-power
    • epsilon-contaminated-priors
    • evidence
    • phase-ii-and-phase-iii-clinical-trials
    • sample-size-determination
  • Mixtures of prior distributions for predictive Bayesian sample size calculations in clinical trials

    Item Type Journal Article
    Author Pierpaolo Brutti
    Author Fulvio De Santis
    Author Stefania Gubbiotti
    Date 2009
    Extra Citation Key: bru09mix tex.citeulike-article-id= 13265772 tex.posted-at= 2014-07-14 14:10:03 tex.priority= 0
    Volume 28
    Pages 2185-2201
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-methods
    • sample-size-estimation
    • predictive-distribution
    • analysis-prior
    • design-prior
    • normal-approximations
    • phase-ii-and-iii-clinical-trials
    • sample-size-re-estimation
  • Effect of Teaching Bayesian Methods Using Learning by Concept vs Learning by Example on Medical Students’ Ability to Estimate Probability of a Diagnosis: A Randomized Clinical Trial

    Item Type Journal Article
    Author John E. Brush
    Author Mark Lee
    Author Jonathan Sherbino
    Author Judith C. Taylor-Fishwick
    Author Geoffrey Norman
    Abstract <h3>Importance</h3><p>Clinicians use probability estimates to make a diagnosis. Teaching students to make more accurate probability estimates could improve the diagnostic process and, ultimately, the quality of medical care.</p><h3>Objective</h3><p>To test whether novice clinicians can be taught to make more accurate bayesian revisions of diagnostic probabilities using teaching methods that apply either explicit conceptual instruction or repeated examples.</p><h3>Design, Setting, and Participants</h3><p>A randomized clinical trial of 2 methods for teaching bayesian updating and diagnostic reasoning was performed. A web-based platform was used for consent, randomization, intervention, and testing of the effect of the intervention. Participants included 61 medical students at McMaster University and Eastern Virginia Medical School recruited from May 1 to September 30, 2018.</p><h3>Interventions</h3><p>Students were randomized to (1) receive explicit conceptual instruction regarding diagnostic testing and bayesian revision (concept group), (2) exposure to repeated examples of cases with feedback regarding posttest probability (experience group), or (3) a control condition with no conceptual instruction or repeated examples.</p><h3>Main Outcomes and Measures</h3><p>Students in all 3 groups were tested on their ability to update the probability of a diagnosis based on either negative or positive test results. Their probability revisions were compared with posttest probability revisions that were calculated using the Bayes rule and known test sensitivity and specificity.</p><h3>Results</h3><p>Of the 61 participants, 22 were assigned to the concept group, 20 to the experience group, and 19 to the control group. Approximate age was 25 years. Two participants were first-year; 37, second-year; 12, third-year; and 10, fourth-year students. Mean (SE) probability estimates of students in the concept group were statistically significantly closer to calculated bayesian probability than the other 2 groups (concept, 0.4%; [0.7%]; experience, 3.5% [0.7%]; control, 4.3% [0.7%];<i>P</i> &lt; .001). Although statistically significant, the differences between groups were relatively modest, and students in all groups performed better than expected, based on prior reports in the literature.</p><h3>Conclusions and Relevance</h3><p>The study showed a modest advantage for students who received theoretical instruction on bayesian concepts. All participants’ probability estimates were, on average, close to the bayesian calculation. These findings have implications for how to teach diagnostic reasoning to novice clinicians.</p><h3>Trial Registration</h3><p>ClinicalTrials.gov identifier:NCT04130607</p>
    Date 2019/12/02
    Language en
    Short Title Effect of Teaching Bayesian Methods Using Learning by Concept vs Learning by Example on Medical Students’ Ability to Estimate Probability of a Diagnosis
    Library Catalog jamanetwork.com
    URL https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2757877
    Accessed 12/21/2019, 7:23:26 AM
    Volume 2
    Pages e1918023-e1918023
    Publication JAMA Network Open
    DOI 10.1001/jamanetworkopen.2019.18023
    Issue 12
    Journal Abbr JAMA Netw Open
    Date Added 12/21/2019, 7:23:26 AM
    Modified 12/21/2019, 7:23:56 AM

    Tags:

    • bayes
    • teaching
    • teaching-mds
    • diagnosis
  • A Bayesian meta-analysis of randomized mega-trials for the choice of thrombolytic agent in acute myocardial infarction

    Item Type Book Section
    Author J. Brophy
    Author L. Joseph
    Editor D. Berry
    Editor D. Stangl
    Date 2000
    Extra Citation Key: bro00bay tex.citeulike-article-id= 13265203 tex.posted-at= 2014-07-14 14:09:51 tex.priority= 0
    Place New York
    Publisher Marcel Dekker
    Pages 83-104
    Book Title Meta-Analysis in Medicine and Health Policy
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-analysis
    • gusto
    • poolability

    Notes:

    • accounting for differences in treatment delivery; adjusting analyses to explicitly account for the possible effect of accelerated dosing, using a hierarchical Bayesian model with bias corrections. Despite this adjustment, the authors found very similar conclusions as in their previous article, and therefore conclude that this does not fully explain the differences observed in the results of GUSTO compared to previous studies.

  • Placing trials in context using Bayesian analysis: GUSTO revisited by Reverend Bayes

    Item Type Journal Article
    Author James M. Brophy
    Author Lawrence Joseph
    Date 1995
    Extra Citation Key: bro95pla tex.citeulike-article-id= 13263819 tex.posted-at= 2014-07-14 14:09:23 tex.priority= 0
    Volume 273
    Pages 871-875
    Publication JAMA
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • meta-analysis
    • gusto
    • poolability
    • t-pa
    • see-bro00bay
    • sensitivity-to-prior
  • A Bayesian analysis of regression models with continuous errors with application to longitudinal studies

    Item Type Journal Article
    Author Lyle D. Broemeling
    Author Peyton Cook
    Date 1997
    Extra Citation Key: bro97bay tex.citeulike-article-id= 13263820 tex.posted-at= 2014-07-14 14:09:23 tex.priority= 0
    Volume 16
    Pages 321-332
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • bayesian-modeling
    • time-series
    • direct-sampling-approach-to-estimating-posterior-density
    • regression-analysis-of-correlated-data
  • The Substitute for p-Values

    Item Type Journal Article
    Author William M. Briggs
    Abstract If it was not obvious before, after reading McShane and Gal, the conclusion is that p-values should be proscribed. There are no good uses for them; indeed, every use either violates frequentist theory, is fallacious, or is based on a misunderstanding. A replacement for p-values is suggested, based on predictive models.
    Date 2017-07
    URL http://dx.doi.org/10.1080/01621459.2017.1311264
    Extra Citation Key: bri17sub tex.citeulike-article-id= 14479856 tex.citeulike-attachment-1= bri17sub.pdf; /pdf/user/harrelfe/article/14479856/1123078/bri17sub.pdf; e2946ca2518f20e15d607a0bccb9accb149c2c19 tex.citeulike-linkout-0= http://dx.doi.org/10.1080/01621459.2017.1311264 tex.citeulike-linkout-1= http://www.tandfonline.com/doi/abs/10.1080/01621459.2017.1311264 tex.day= 3 tex.posted-at= 2017-11-21 14:33:28 tex.priority= 0 tex.publisher= Taylor & Francis
    Volume 112
    Pages 897-898
    Publication JASA
    DOI 10.1080/01621459.2017.1311264
    Issue 519
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • teaching-mds
    • teaching-statisticians
    • p-values
  • Biostatistics and Bayes (with discussion)

    Item Type Journal Article
    Author Norman Breslow
    Date 1990
    Extra Citation Key: bre90bio tex.citeulike-article-id= 13263806 tex.posted-at= 2014-07-14 14:09:23 tex.priority= 0
    Volume 5
    Pages 269-298
    Publication Stat Sci
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • sequential-monitoring
    • bioequivalence
    • model-selection-p-281
  • Motivating sample sizes in adaptive Phase I trials via Bayesian posterior credible intervals

    Item Type Journal Article
    Author Thomas M. Braun
    Abstract In contrast with typical Phase III clinical trials, there is little existing methodology for determining the appropriate numbers of patients to enroll in adaptive Phase I trials. And, as stated by Dennis Lindley in a more general context, ” [t]he simple practical question of 'What size of sample should I take' is often posed to a statistician, and it is a question that is embarrassingly difficult to answer.” Historically, simulation has been the primary option for determining sample sizes for adaptive Phase I trials, and although useful, can be problematic and time-consuming when a sample size is needed relatively quickly. We propose a computationally fast and simple approach that uses Beta distributions to approximate the posterior distributions of DLT rates of each dose and determines an appropriate sample size through posterior coverage rates. We provide sample sizes produced by our methods for a vast number of realistic Phase I trial settings and demonstrate that our sample sizes are generally larger than those produced by a competing approach that is based upon the nonparametric optimal design.
    URL http://dx.doi.org/10.1111/biom.12872
    Extra Citation Key: bra18mot tex.citeulike-article-id= 14548317 tex.citeulike-linkout-0= http://dx.doi.org/10.1111/biom.12872 tex.posted-at= 2018-03-13 19:45:31 tex.priority= 2
    Pages n/a
    Publication Biom
    DOI 10.1111/biom.12872
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • sample-size
    • adaptive-design
    • drug-development
  • Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology

    Item Type Journal Article
    Author Werner Brannath
    Author Emmanuel Zuber
    Author Michael Branson
    Author Frank Bretz
    Author Paul Gallo
    Author Martin Posch
    Author Amy Racine-Poon
    Date 2009
    Extra Citation Key: bra09con tex.citeulike-article-id= 13265757 tex.posted-at= 2014-07-14 14:10:03 tex.priority= 0
    Volume 28
    Pages 1445-1463
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • time-to-event-data
    • biomarker
    • bayesian-adapative-design
    • closure-principle
    • combination-test
    • flexible-design
    • group-sequential-design
    • posterior-probability
    • predictive-power
    • sub-population-selection

    Notes:

    • nice display of how to formalize the use of posterior probabilities for deciding the next step in the trial;RCT

  • Breaking the Bayesian Ice with Preclinical Discovery Biologists by Predicting Inadequate Animal Enrolment

    Item Type Journal Article
    Author Thomas E. Bradstreet
    Abstract An initial proposal was made to start 30 monkeys in the run-in period of a preclinical translational research study, to have 24 or more animals qualify for randomization in the subsequent treatment period. Based upon data from previous studies, Bayesian posterior prediction indicated that successful enrolment was highly unlikely. At least 67 animals were required to achieve an acceptable posterior predictive probability of success. Importantly, we leveraged these feasibility analyses to introduce our preclinical scientist collaborators to a Bayesian strategy for probability-based decision making. We provided them with a generous helping of graphics to effectively and efficiently illustrate Bayesian concepts and methods. We present our 4P strategy for collaboration with preclinical scientists: patience, persistence, positioning, and privilege. We discuss the alignment of the Bayesian and 4P strategies with goals common to pharmaceutical researchers: scientific innovation; stochastic intelligence and statistical literacy of team members; team collaboration and collegial partnerships; ethical acuity; and fiscal stewardship. Our article is as much about successfully reaching out to preclinical scientists, and introducing them to the Bayesian strategy, as it is about that strategy successfully addressing the animal enrolment question. This article is written at a statistical level accessible to both preclinical scientists and statisticians.
    Date July 3, 2021
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/19466315.2020.1799856
    Accessed 11/12/2021, 1:45:29 PM
    Extra Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/19466315.2020.1799856
    Volume 13
    Pages 344-354
    Publication Statistics in Biopharmaceutical Research
    DOI 10.1080/19466315.2020.1799856
    Issue 3
    ISSN null
    Date Added 11/12/2021, 1:45:29 PM
    Modified 11/12/2021, 1:45:50 PM

    Tags:

    • bayes
    • teaching-mds
    • prior-elicitation
    • sequential
  • Simulation-Based Prior Knowledge Elicitation for Parametric Bayesian Models

    Item Type Preprint
    Author Florence Bockting
    Author Stefan T. Radev
    Author Paul-Christian Bürkner
    Abstract A central characteristic of Bayesian statistics is the ability to consistently incorporate prior knowledge into various modeling processes. In this paper, we focus on translating domain expert knowledge into corresponding prior distributions over model parameters, a process known as prior elicitation. Expert knowledge can manifest itself in diverse formats, including information about raw data, summary statistics, or model parameters. A major challenge for existing elicitation methods is how to effectively utilize all of these different formats in order to formulate prior distributions that align with the expert's expectations, regardless of the model structure. To address these challenges, we develop a simulation-based elicitation method that can learn the hyperparameters of potentially any parametric prior distribution from a wide spectrum of expert knowledge using stochastic gradient descent. We validate the effectiveness and robustness of our elicitation method in four representative case studies covering linear models, generalized linear models, and hierarchical models. Our results support the claim that our method is largely independent of the underlying model structure and adaptable to various elicitation techniques, including quantile-based, moment-based, and histogram-based methods.
    Date 2023-08-22
    Library Catalog arXiv.org
    URL http://arxiv.org/abs/2308.11672
    Accessed 8/25/2023, 5:57:22 PM
    Extra arXiv:2308.11672 [stat]
    DOI 10.48550/arXiv.2308.11672
    Repository arXiv
    Archive ID arXiv:2308.11672
    Date Added 8/25/2023, 5:57:22 PM
    Modified 8/25/2023, 5:57:51 PM

    Tags:

    • bayes
    • prior
    • prior-elicitation
    • simulation
  • Bayesian analysis of realistically complex models

    Item Type Journal Article
    Author N. G. Best
    Author D. J. Spiegelhalter
    Author A. Thomas
    Author C. E. G. Brayne
    Date 1996
    Extra Citation Key: bes96bay tex.citeulike-article-id= 13263761 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0
    Volume 159
    Pages 323-342
    Publication J Roy Stat Soc A
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-methods
    • gibbs-sampling
    • random-effects-model
    • informative-dropout
    • conditional-independence
    • graphical-models
    • repeated-ordinal-categorical-responses
  • Decision making during a phase III randomized controlled trial

    Item Type Journal Article
    Author Donald A. Berry
    Author Mark C. Wolff
    Author David Sack
    Date 1994
    URL http://dx.doi.org/10.1016/0197-2456(94)90033-7
    Extra Citation Key: ber94dec tex.citeulike-article-id= 13263754 tex.citeulike-linkout-0= http://dx.doi.org/10.1016/0197-2456(94)90033-7 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0
    Volume 15
    Pages 360-378
    Publication Controlled Clin Trials
    DOI 10.1016/0197-2456(94)90033-7
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • rct
    • bayesian-inference
    • study-design
  • Bayesian Statistics and the Efficiency and Ethics of Clinical Trials

    Item Type Journal Article
    Author Donald A. Berry
    Abstract The Bayesian approach is being used increasingly in medical research. The flexibility of the Bayesian approach allows for building designs of clinical trials that have good properties of any desired sort. Examples include maximizing effective treatment of patients in the trial, maximizing information about the slope of a dose–response curve, minimizing costs, minimizing the number of patients treated, minimizing the length of the trial and combinations of these desiderata. They also include standard frequentist operating characteristics when these are important considerations. Posterior probabilities are updated via Bayes’ theorem on the basis of accumulating data. These are used to effect modifications of the trial’s course, including stopping accrual, extending accrual beyond that originally planned, dropping treatment arms, adding arms, etc. An important aspect of the approach I advocate is modeling the relationship between a trial’s primary endpoint and early indications of patient performance—auxiliary endpoints. This has several highly desirable consequences. One is that it improves the efficiency of adaptive trials because information is available sooner than otherwise.
    Date 2004-02
    Language en
    Library Catalog Project Euclid
    URL https://projecteuclid.org/euclid.ss/1089808281
    Accessed 12/21/2019, 8:11:06 AM
    Extra MR: MR2086326 Zbl: 1057.62096
    Volume 19
    Pages 175-187
    Publication Statistical Science
    DOI 10.1214/088342304000000044
    Issue 1
    Journal Abbr Statist. Sci.
    ISSN 0883-4237, 2168-8745
    Date Added 12/21/2019, 8:11:06 AM
    Modified 12/21/2019, 8:11:46 AM

    Tags:

    • bayes
    • rct
    • adaptive
    • ethics
    • sequential
  • Adaptive trials and Bayesian statistics in drug development (with discussion)

    Item Type Journal Article
    Author Donald A. Berry
    Date 2001
    Extra Citation Key: ber01ada tex.citeulike-article-id= 13265268 tex.posted-at= 2014-07-14 14:09:53 tex.priority= 0
    Volume 9
    Pages 1-11
    Publication Biopharm Rep ASA
    Issue 2
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • bayesian-methods
    • adaptive-design
    • drug-development
    • play-the-winner
    • seamless-designs

    Notes:

    • excellent motivation for use of Bayesian methods in clinical trials;seamless Phase II-Phase II trials; comments by Chi, Hung, O'Neill;trial vs. process;holdup from using more adaptive designs is more due to logistics/blinding than to frequentist methods (from discussants)

  • Teaching elementary Bayesian statistics with real applications in science

    Item Type Journal Article
    Author Donald A. Berry
    Date 1997
    Extra Citation Key: ber97tea tex.citeulike-article-id= 13263759 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0
    Volume 51
    Pages 241-246
    Publication Am Statistician
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • teaching
    • scientific-approach
  • Interim analysis in clinical trials: The role of the likelihood principle

    Item Type Journal Article
    Author Donald A. Berry
    Date 1987
    URL http://dx.doi.org/10.1080/00031305.1987.10475458
    Extra Citation Key: ber87int tex.citeulike-article-id= 13263745 tex.citeulike-linkout-0= http://dx.doi.org/10.1080/00031305.1987.10475458 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0
    Volume 41
    Pages 117-122
    Publication Am Statistician
    DOI 10.1080/00031305.1987.10475458
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • study-design
    • sequential-monitoring
    • conditionalism
  • Bayesian clinical trials

    Item Type Journal Article
    Author Donald A. Berry
    Date 2006
    Extra Citation Key: ber06bay tex.citeulike-article-id= 13265478 tex.posted-at= 2014-07-14 14:09:57 tex.priority= 0 Editorial, p. 3
    Volume 5
    Pages 27-36
    Publication Nat Rev
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • rct
    • teaching-mds
    • bayesian-methods
    • review

    Notes:

    • excellent review of Bayesian approaches in clinical trials; "The greatest virtue of the traditional approach may be its extreme rigour and narrowness of focus to the experiment at hand, but a side effect of this virtue is inflexibility, which in turn limits innovation in the design and analysis of clinical trials. ... The set of `other possible results' depends on the experimental design. ... Everything that is known is taken as given and all probabilities are calculated conditionally on known values. ... in contrast to the frequentist approach, only the probabilities of the observed results matter. ... The continuous learning that is possible in the Bayesian approach enables investigators to modify trials in midcourse. ... it is possible to learn from small samples, depending on the results, ... it is possible to adapt to what is learned to enable better treatment of patients. ... subjectivity in prior distributions is explicit and open to examination (and critique) by all. ... The Bayesian approach has several advantages in drug development. One is the process of updating knowledge gradually rather than restricting revisions in study design to large, discrete steps measured in trials or phases."

  • Multiple comparisons, multiple tests, and data dredging: A Bayesian perspective

    Item Type Book Section
    Author Donald A. Berry
    Editor J. M. Bernardo
    Editor M. H. DeGroot
    Editor D. V. Lindley
    Editor A. F. M. Smith
    Date 1988
    Extra Citation Key: ber88mul tex.citeulike-article-id= 13263748 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0
    Volume 3
    Publisher Oxford University Press
    Pages 79-94
    Book Title Bayesian Statistics
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • multiple-comparisons
    • multiple-tests
    • data-dredging

    Notes:

    • if want to dredge data to generate hypotheses one still needs another dataset to test the hypotheses

  • Unified frequentist and Bayesian testing of a precise hypothesis (with discussion)

    Item Type Journal Article
    Author J. O. Berger
    Author B. Boukai
    Author Y. Wang
    Date 1997
    Extra Citation Key: ber97uni tex.citeulike-article-id= 13263760 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0
    Volume 12
    Pages 133-160
    Publication Stat Sci
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • misinterpretation-of-p-values

    Notes:

    • "no decision" region;error probability;Bayes factor;likelihood ratio;"in situations ... involving testing a precise null hypothesis, a P-value of 0.05 essentially does not provide any evidence against the null hypothesis";for a one-sample problem where the data are normally distributed with known variance and unknown mean  and H_0: =_0 vs. H_1:  _0, P=0.05 may correspond to a probability of H_0 being true of 0.5

  • Testing a point null hypothesis: The irreconcilability of P-values and evidence

    Item Type Journal Article
    Author J. Berger
    Author T. Sellke
    Date 1987
    Extra Citation Key: ber87tes tex.citeulike-article-id= 13263747 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0
    Volume 82
    Pages 112-139
    Publication J Am Stat Assoc
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • evidence
    • p-value
    • point-null-hypothesis
  • Testing Precise Hypotheses

    Item Type Journal Article
    Author James O. Berger
    Author Mohan Delampady
    Abstract Testing of precise (point or small interval) hypotheses is reviewed, with special emphasis placed on exploring the dramatic conflict between conditional measures (Bayes factors and posterior probabilities) and the classical P-value (or observed significance level). This conflict is highlighted by finding lower bounds on the conditional measures over wide classes of priors, in normal and binomial situations, lower bounds, which are much larger than the P-value; this leads to the recommendation of several alternatives to P-values. Results are also given concerning the validity of approximating an interval null by a point null. The overall discussion features critical examination of issues such as the probability of objective testing and the possibility of testing from confidence sets.
    Date 1987/08
    Library Catalog Project Euclid
    URL https://projecteuclid.org/journals/statistical-science/volume-2/issue-3/Testing-Precise-Hypotheses/10.1214/ss/1177013238.full
    Accessed 4/6/2022, 7:37:31 AM
    Extra Publisher: Institute of Mathematical Statistics
    Volume 2
    Pages 317-335
    Publication Statistical Science
    DOI 10.1214/ss/1177013238
    Issue 3
    ISSN 0883-4237, 2168-8745
    Date Added 4/6/2022, 7:37:31 AM
    Modified 4/6/2022, 7:38:03 AM

    Tags:

    • bayes
    • p-value

    Notes:

    • Quote from Section 4.6:

      Some statisticians argue that the implied logic concerning a small P-value is compelling: “Either H0 is true and a rare event has occurred, or H0 is false.”  One could again argue against this reasoning as addressing the wrong question, but there is a more obvious major flaw: the “rare event” whose probability is being calculated under H0 is not the event of observing the actual data x0, but the event E = {possible data x: |T(x)| >= |T(x0)|}.  The inclusion of all data “more extreme” than the actual x0 is a curious step, and one which we have seen no remotely convincing justification. … the “logic of surprise” cannot differentiate between x0 and E …

  • Statistical analysis and the illusion of objectivity (Letters to editor p. 430-433)

    Item Type Journal Article
    Author James O. Berger
    Author Donald A. Berry
    Date 1988
    Extra Citation Key: ber88sta tex.citeulike-article-id= 13263749 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0
    Volume 76
    Pages 159-165
    Publication Am Scientist
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • p-values
  • Robust Bayes and Empirical Bayes Analysis with ε-Contaminated Priors

    Item Type Journal Article
    Author James Berger
    Author L. Mark Berliner
    Abstract For Bayesian analysis, an attractive method of modelling uncertainty in the prior distribution is through use of ε-contamination classes, i.e., classes of distributions which have the form π = (1 - ε)π0 + ε q, π0 being the base elicited prior, q being a "contamination," and ε reflecting the amount of error in π0 that is deemed possible. Classes of contaminations that are considered include (i) all possible contaminations, (ii) all symmetric, unimodal contaminations, and (iii) all contaminations such that π is unimodal. Two issues in robust Bayesian analysis are studied. The first is that of determining the range of posterior probabilities of a set as π ranges over the ε-contamination class. The second, more extensively studied, issue is that of selecting, in a data dependent fashion, a "good" prior distribution (the Type-II maximum likelihood prior) from the ε-contamination class, and using this prior in the subsequent analysis. Relationships and applications to empirical Bayes analysis are also discussed.
    Date 1986
    Archive JSTOR
    Library Catalog JSTOR
    URL https://www.jstor.org/stable/2241230
    Accessed 8/23/2019, 8:51:45 AM
    Volume 14
    Pages 461-486
    Publication The Annals of Statistics
    Issue 2
    ISSN 0090-5364
    Date Added 8/23/2019, 8:51:46 AM
    Modified 8/23/2019, 8:52:18 AM

    Tags:

    • bayes
    • prior
  • A Gentle Introduction to the Comparison Between Null Hypothesis Testing and Bayesian Analysis: Reanalysis of Two Randomized Controlled Trials

    Item Type Journal Article
    Author Marcus Bendtsen
    Abstract The debate on the use and misuse of P values has risen and fallen throughout their almost century-long existence in scientific discovery. Over the past few years, the debate has again received front-page attention, particularly through the public reminder by the American Statistical Association on how P values should be used and interpreted. At the core of the issue lies a fault in the way that scientific evidence is dichotomized and research is subsequently reported, and this fault is exacerbated by researchers giving license to statistical models to do scientific inference. This paper highlights a different approach to handling the evidence collected during a randomized controlled trial, one that does not dichotomize, but rather reports the evidence collected. Through the use of a coin flipping experiment and reanalysis of real-world data, the traditional approach of testing null hypothesis significance is contrasted with a Bayesian approach. This paper is meant to be understood by those who rely on statistical models to draw conclusions from data, but are not statisticians and may therefore not be able to grasp the debate that is primarily led by statisticians. [J Med Internet Res 2018;20(10):e10873]
    Date 2018
    Language en
    Short Title A Gentle Introduction to the Comparison Between Null Hypothesis Testing and Bayesian Analysis
    Library Catalog www.jmir.org
    URL https://www.jmir.org/2018/10/e10873/
    Accessed 3/16/2020, 3:09:03 PM
    Extra Company: Journal of Medical Internet Research Distributor: Journal of Medical Internet Research Institution: Journal of Medical Internet Research Label: Journal of Medical Internet Research Publisher: JMIR Publications Inc., Toronto, Canada
    Volume 20
    Pages e10873
    Publication Journal of Medical Internet Research
    DOI 10.2196/10873
    Issue 10
    Date Added 3/16/2020, 3:09:03 PM
    Modified 3/16/2020, 3:09:43 PM

    Tags:

    • bayes
    • teaching
    • teaching-mds

    Notes:

    • Using priors forces us to be more specific and explicit about what we mean when we say that something is unknown... the Bayesian approach does not attempt to identify a fixed value for the parameters and dichotomize the world into significant and nonsignificant, but rather relies on the researcher to do the scientific inference and not to delegate this obligation to the statistical model... the NHST approach is rooted in the idea of being able to redo the experiment many times (so as to get a sampling distribution).  Even if we can rely on theoretical results to get this sampling distribution without actually going back in time and redoing the experiment, the underlying idea can be somewhat problematic.  What do we mean by redoing an experiment? Can we redo a randomized controlled trial while keeping all things equal and recruiting a new sample from the study population?... Once we remove ourselves from the dichotomization of evidence, other things start to take precedence: critically assessing the models chosen, evaluating the quality of the data, interpreting the real-world impact of the results, etc.

  • A new perspective on priors for generalized linear models

    Item Type Journal Article
    Author Edward J. Bedrick
    Author Ronald Christensen
    Author Wesley Johnson
    Date 1996
    Extra Citation Key: bed96new tex.citeulike-article-id= 13263734 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0
    Volume 91
    Pages 1450-1460
    Publication J Am Stat Assoc
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • conditional-mean-priors
    • data-augmentation
    • choice-of-prior-distribution
  • Bayesian binomial regression: Predicting survival at a trauma center

    Item Type Journal Article
    Author Edward J. Bedrick
    Author Ronald Christensen
    Author Wesley Johnson
    Date 1997
    Extra Citation Key: bed97bay tex.citeulike-article-id= 13263735 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0
    Volume 51
    Pages 211-218
    Publication Am Statistician
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • case-deletion-of-augmented-prior-data-to-analyze-sensitivity-to-choice-of-prior
    • choice-of-prior
    • conditional-mean-priors
    • data-augmentation
    • logistic-model
    • simulation-of-posterior
  • Comparative Statistical Inference

    Item Type Book
    Author V. Barnett
    Date 1982
    Extra Citation Key: bar82com tex.citeulike-article-id= 13263727 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0
    Publisher Wiley
    Edition Second
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • comparison
    • prior

    Notes:

    • This is a nice comparative account of frequentist and Bayesian methods. In particular, it contains a chapter on the various approaches to prior specification

  • Bayesian Causality

    Item Type Journal Article
    Author Pierre Baldi
    Author Babak Shahbaba
    Abstract Although no universally accepted definition of causality exists, in practice one is often faced with the question of statistically assessing causal relationships in different settings. We present a uniform general approach to causality problems derived from the axiomatic foundations of the Bayesian statistical framework. In this approach, causality statements are viewed as hypotheses, or models, about the world and the fundamental object to be computed is the posterior distribution of the causal hypotheses, given the data and the background knowledge. Computation of the posterior, illustrated here in simple examples, may involve complex probabilistic modeling but this is no different than in any other Bayesian modeling situation. The main advantage of the approach is its connection to the axiomatic foundations of the Bayesian framework, and the general uniformity with which it can be applied to a variety of causality settings, ranging from specific to general cases, or from causes of effects to effects of causes.
    Date August 12, 2019
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1080/00031305.2019.1647876
    Accessed 8/30/2019, 11:14:58 AM
    Volume 0
    Pages 1-9
    Publication The American Statistician
    DOI 10.1080/00031305.2019.1647876
    Issue 0
    ISSN 0003-1305
    Date Added 8/30/2019, 11:14:58 AM
    Modified 8/30/2019, 11:15:43 AM

    Tags:

    • bayes
    • causal-inference
    • causality
    • causal-model
  • A Bayesian approach for event predictions in clinical trials with time-to-event outcomes

    Item Type Journal Article
    Author Paul Aubel
    Author Marine Antigny
    Author Ronan Fougeray
    Author Frédéric Dubois
    Author Gaëlle Saint-Hilary
    Abstract In clinical trials with time-to-event outcome as the primary endpoint, the end of study date is often based on the number of observed events, which drives the statistical power and the sample size calculation. It is of great value for study sponsors to have a good understanding of the recruitment process and the event milestones to manage the logistical tasks, which require a considerable amount of resources. The objective of the proposed statistical approach is to predict, as accurately as possible, the timing of an analysis planned once a target number of events is collected. The method takes into account the enrollment, the time to event, and the time to censor processes, using Weibull models in a Bayesian framework. We also consider a possible delay in the event reporting by the investigators, and covariates may also be included. Several metrics can be obtained, such as the probability of study completion at specific timepoints or the credible interval of the date of study completion. The approach was applied to oncology trials, with progression-free survival as primary outcome. A retrospective analysis shows the accuracy of the approach on these examples, as well as the benefit of updating the predictive probability of study completion as data are accumulating or new information becomes available. We also evaluated the performances of the proposed method in a comprehensive simulation study.
    Date 2021
    Language en
    Library Catalog Wiley Online Library
    URL http://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9186
    Accessed 9/21/2021, 9:13:44 AM
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9186
    Volume n/a
    Publication Statistics in Medicine
    DOI 10.1002/sim.9186
    Issue n/a
    ISSN 1097-0258
    Date Added 9/21/2021, 9:13:44 AM
    Modified 9/21/2021, 9:14:33 AM

    Tags:

    • bayes
    • rct
    • prediction
    • implementation
    • recruitment
  • Therapeutic Anticoagulation with Heparin in Noncritically Ill Patients with Covid-19

    Item Type Journal Article
    Author ATTACC Investigators
    Date August 4, 2021
    Library Catalog Taylor and Francis+NEJM
    URL https://doi.org/10.1056/NEJMoa2105911
    Accessed 8/7/2021, 2:31:29 PM
    Extra Publisher: Massachusetts Medical Society _eprint: https://doi.org/10.1056/NEJMoa2105911
    Volume 0
    Pages null
    Publication New England Journal of Medicine
    DOI 10.1056/NEJMoa2105911
    Issue 0
    ISSN 0028-4793
    Date Added 8/7/2021, 2:31:29 PM
    Modified 8/7/2021, 2:33:06 PM

    Tags:

    • bayes
    • rct
    • teaching-mds
  • Bayesian statistics in medicine: A 25 year review

    Item Type Journal Article
    Author Deborah Ashby
    Date 2006
    Extra Citation Key: ash06bay tex.citeulike-article-id= 13265537 tex.posted-at= 2014-07-14 14:09:58 tex.priority= 0
    Volume 25
    Pages 3589-3631
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-methods
    • review
    • multiple-uses-of-bayes
  • Non-parametric Bayesian approach to hazard regression: A case study with a large number of missing covariate values

    Item Type Journal Article
    Author Elja Arjas
    Author Liping Liu
    Date 1996
    Extra Citation Key: arj96non tex.citeulike-article-id= 13263706 tex.posted-at= 2014-07-14 14:09:21 tex.priority= 0
    Volume 15
    Pages 1757-1770
    Publication Stat Med
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • missing-covariables
    • non-parametric-hazard-function-estimation
    • non-ph
  • Recommendations for Statistical Reporting in Cardiovascular Medicine: A Special Report From the American Heart Association

    Item Type Journal Article
    Author Andrew D. Althouse
    Author Jennifer E. Below
    Author Brian L. Claggett
    Author Nancy J. Cox
    Author James A. de Lemos
    Author Rahul C. Deo
    Author Sue Duval
    Author Rory Hachamovitch
    Author Sanjay Kaul
    Author Scott W. Keith
    Author Eric Secemsky
    Author Armando Teixeira-Pinto
    Author Veronique L. Roger
    Author null null
    Abstract Statistical analyses are a crucial component of the biomedical research process and are necessary to draw inferences from biomedical research data. The application of sound statistical methodology is a prerequisite for publication in the American Heart Association (AHA) journal portfolio. The objective of this document is to summarize key aspects of statistical reporting that might be most relevant to the authors, reviewers, and readership of AHA journals. The AHA Scientific Publication Committee convened a task force to inventory existing statistical standards for publication in biomedical journals and to identify approaches suitable for the AHA journal portfolio. The experts on the task force were selected by the AHA Scientific Publication Committee, who identified 12 key topics that serve as the section headers for this document. For each topic, the members of the writing group identified relevant references and evaluated them as a resource to make the standards summarized herein. Each section was independently reviewed by an expert reviewer who was not part of the task force. Expert reviewers were also permitted to comment on other sections if they chose. Differences of opinion were adjudicated by consensus. The standards presented in this report are intended to serve as a guide for high-quality reporting of statistical analyses methods and results.
    Date July 27, 2021
    Short Title Recommendations for Statistical Reporting in Cardiovascular Medicine
    Library Catalog ahajournals.org (Atypon)
    URL https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.121.055393
    Accessed 1/11/2022, 7:37:12 AM
    Volume 144
    Pages e70-e91
    Publication Circulation
    DOI 10.1161/CIRCULATIONAHA.121.055393
    Issue 4
    Date Added 1/11/2022, 7:37:40 AM
    Modified 1/11/2022, 7:37:40 AM

    Tags:

    • bayes
    • teaching-mds
    • reporting
    • reporting-statistical-results
    • guidelines
  • Teaching Bayesian statistics using sampling methods and MINITAB

    Item Type Journal Article
    Author James H. Albert
    Date 1993
    Extra Citation Key: alb93tea tex.citeulike-article-id= 13263681 tex.posted-at= 2014-07-14 14:09:21 tex.priority= 0
    Volume 47
    Pages 182-191
    Publication Am Statistician
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • sampling-importance-resampling
    • teaching
    • weighted-bootstrap
  • Sample size determination: a review

    Item Type Journal Article
    Author C. J. Adcock
    Date 1997
    Extra Citation Key: adc97sam tex.citeulike-article-id= 13265277 tex.posted-at= 2014-07-14 14:09:53 tex.priority= 0
    Volume 46
    Pages 261-283
    Publication The Statistician
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayes
    • bayesian-inference
    • sample-size
    • bayesian-sample-size-estimation
    • average-coverage-criterion
    • bayes-factors
    • mcnemars-test
    • multinomial-distribution
  • Simple Bayesian analysis in clinical trials: A tutorial

    Item Type Journal Article
    Author Keith Abrams
    Author Deborah Ashby
    Author Doug Errington
    Date 1994
    Extra Citation Key: abr94sim tex.citeulike-article-id= 13263671 tex.posted-at= 2014-07-14 14:09:20 tex.priority= 0
    Volume 15
    Pages 349-359
    Publication Controlled Clin Trials
    Date Added 7/7/2018, 1:38:33 PM
    Modified 11/8/2019, 8:01:59 AM

    Tags:

    • bayesian-inference
    • study-design