| 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 |
| URL | https://www.nature.com/articles/s41573-023-00638-0 |
| Rights | 2023 Springer Nature Limited |
| Pages | 1-16 |
| Publication | Nature Reviews Drug Discovery |
| ISSN | 1474-1784 |
| Date | 2023-02-15 |
| Extra | Publisher: Nature Publishing Group |
| Journal Abbr | Nat Rev Drug Discov |
| DOI | 10.1038/s41573-023-00638-0 |
| Accessed | 2/16/2023, 2:20:27 PM |
| Library Catalog | www.nature.com |
| Language | en |
| 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. |
| Short Title | Application of Bayesian approaches in drug development |
| Date Added | 2/16/2023, 2:20:27 PM |
| Modified | 2/16/2023, 2:21:02 PM |
| 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 |
| URL | https://evidence.nejm.org/doi/10.1056/EVIDoa2200196 |
| Volume | 2 |
| Issue | 1 |
| Pages | EVIDoa2200196 |
| Publication | NEJM Evidence |
| Date | 2022-12-27 |
| Extra | Publisher: Massachusetts Medical Society |
| DOI | 10.1056/EVIDoa2200196 |
| Accessed | 12/30/2022, 3:21:36 AM |
| Library Catalog | evidence.nejm.org (Atypon) |
| 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 Added | 12/30/2022, 3:21:36 AM |
| Modified | 12/30/2022, 3:22:54 AM |
| Type | Journal Article |
|---|---|
| Author | Armando Turchetta |
| Author | Erica E.M. Moodie |
| Author | David A. Stephens |
| Author | Sylvie D. Lambert |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13813 |
| Volume | n/a |
| Issue | n/a |
| Publication | Biometrics |
| ISSN | 1541-0420 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13813 |
| DOI | 10.1111/biom.13813 |
| Accessed | 12/14/2022, 9:57:31 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 |
| Date Added | 12/14/2022, 9:57:31 AM |
| Modified | 12/14/2022, 9:58:15 AM |
| 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 |
| URL | https://doi.org/10.1007/s43441-022-00483-0 |
| Publication | Therapeutic Innovation & Regulatory Science |
| ISSN | 2168-4804 |
| Date | 2022-12-02 |
| Journal Abbr | Ther Innov Regul Sci |
| DOI | 10.1007/s43441-022-00483-0 |
| Accessed | 12/2/2022, 3:43:43 PM |
| Library Catalog | Springer Link |
| Language | en |
| 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 Added | 12/2/2022, 3:43:43 PM |
| Modified | 12/2/2022, 3:44:27 PM |
Examples of use of Bayes at FDA CDER and CBER
| Type | Journal Article |
|---|---|
| Author | A. Racine |
| Author | A. P. Grieve |
| Author | H. Fluhler |
| Author | A. F. M. Smith |
| URL | https://www.jstor.org/stable/2347264 |
| Volume | 35 |
| Issue | 2 |
| Pages | 93-150 |
| Publication | Journal of the Royal Statistical Society. Series C (Applied Statistics) |
| ISSN | 0035-9254 |
| Date | 1986 |
| Extra | Publisher: [Wiley, Royal Statistical Society] |
| DOI | 10.2307/2347264 |
| Accessed | 11/10/2022, 7:15:11 AM |
| Library Catalog | JSTOR |
| 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. |
| Short Title | Bayesian Methods in Practice |
| Date Added | 11/10/2022, 7:15:11 AM |
| Modified | 11/10/2022, 7:15:36 AM |
| Type | Journal Article |
|---|---|
| Author | Zitong Lu |
| Author | Zhi Geng |
| Author | Wei Li |
| Author | Shengyu Zhu |
| Author | Jinzhu Jia |
| URL | https://doi.org/10.1093/biomet/asac038 |
| Pages | asac038 |
| Publication | Biometrika |
| ISSN | 1464-3510 |
| Date | 2022-07-09 |
| Journal Abbr | Biometrika |
| DOI | 10.1093/biomet/asac038 |
| Accessed | 8/18/2022, 7:01:14 AM |
| Library Catalog | Silverchair |
| 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 Added | 8/18/2022, 7:01:44 AM |
| Modified | 8/18/2022, 7:02:47 AM |
| Type | Journal Article |
|---|---|
| Author | Wei Hong |
| Author | Sue-Anne McLachlan |
| Author | Melissa Moore |
| Author | Robert K. Mahar |
| URL | https://doi.org/10.1186/s12874-022-01603-y |
| Volume | 22 |
| Issue | 1 |
| Pages | 133 |
| Publication | BMC Medical Research Methodology |
| ISSN | 1471-2288 |
| Date | 2022-05-04 |
| Journal Abbr | BMC Medical Research Methodology |
| DOI | 10.1186/s12874-022-01603-y |
| Accessed | 5/5/2022, 6:25:41 AM |
| Library Catalog | BioMed Central |
| 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. |
| Short Title | Improving clinical trials using Bayesian adaptive designs |
| Date Added | 5/5/2022, 6:25:41 AM |
| Modified | 5/5/2022, 6:26:44 AM |
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.
| Type | Journal Article |
|---|---|
| Author | Shirin Golchi |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/cjs.11699 |
| Volume | n/a |
| Issue | n/a |
| Publication | Canadian Journal of Statistics |
| ISSN | 1708-945X |
| Date | 2022 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjs.11699 |
| DOI | 10.1002/cjs.11699 |
| Accessed | 4/17/2022, 7:25:54 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 4/17/2022, 7:25:54 AM |
| Modified | 4/17/2022, 7:26:40 AM |
| Type | Journal Article |
|---|---|
| Author | Spencer Hansen |
| Author | Ken Rice |
| URL | https://doi.org/10.1080/00031305.2022.2050299 |
| Volume | 0 |
| Issue | 0 |
| Pages | 1-9 |
| Publication | The American Statistician |
| ISSN | 0003-1305 |
| Date | 2022-03-08 |
| Extra | Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00031305.2022.2050299 |
| DOI | 10.1080/00031305.2022.2050299 |
| Accessed | 4/9/2022, 11:06:10 AM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 4/9/2022, 11:06:10 AM |
| Modified | 4/9/2022, 11:07:21 AM |
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
| Type | Journal Article |
|---|---|
| Author | James O. Berger |
| Author | Mohan Delampady |
| URL | https://projecteuclid.org/journals/statistical-science/volume-2/issue-3/Testing-Precise-Hypotheses/10.1214/ss/1177013238.full |
| Volume | 2 |
| Issue | 3 |
| Pages | 317-335 |
| Publication | Statistical Science |
| ISSN | 0883-4237, 2168-8745 |
| Date | 1987/08 |
| Extra | Publisher: Institute of Mathematical Statistics |
| DOI | 10.1214/ss/1177013238 |
| Accessed | 4/6/2022, 7:37:31 AM |
| Library Catalog | Project Euclid |
| 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 Added | 4/6/2022, 7:37:31 AM |
| Modified | 4/6/2022, 7:38:03 AM |
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 …
| Type | Journal Article |
|---|---|
| Author | Rianne de Heide |
| Author | Peter D. Grünwald |
| URL | https://doi.org/10.3758/s13423-020-01803-x |
| Volume | 28 |
| Issue | 3 |
| Pages | 795-812 |
| Publication | Psychonomic Bulletin & Review |
| ISSN | 1531-5320 |
| Date | 2021-06-01 |
| Journal Abbr | Psychon Bull Rev |
| DOI | 10.3758/s13423-020-01803-x |
| Accessed | 3/24/2022, 11:11:58 AM |
| Library Catalog | Springer Link |
| Language | en |
| 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 Added | 3/24/2022, 11:12:03 AM |
| Modified | 3/24/2022, 11:12:56 AM |
Interesting taxonomy of priors
| Type | Journal Article |
|---|---|
| Author | Jakob Richter |
| Author | Tim Friede |
| Author | Jörg Rahnenführer |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.202000389 |
| Volume | n/a |
| Issue | n/a |
| Publication | Biometrical Journal |
| ISSN | 1521-4036 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202000389 |
| DOI | 10.1002/bimj.202000389 |
| Accessed | 2/28/2022, 11:57:27 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 2/28/2022, 11:57:27 AM |
| Modified | 2/28/2022, 11:58:36 AM |
| Type | Journal Article |
|---|---|
| Author | Zihang Lu |
| Author | Wendy Lou |
| URL | https://www.degruyter.com/document/doi/10.1515/ijb-2020-0130/html?_llca=transfer%3A6c4e2be67680df8697a36347096dc061&_llch=4a014e430be2a0321edc05c36ca125ee393e28b730c6fd6acf796c4547770148 |
| Publication | The International Journal of Biostatistics |
| ISSN | 1557-4679 |
| Date | 2021-03-24 |
| Extra | Publisher: De Gruyter |
| DOI | 10.1515/ijb-2020-0130 |
| Accessed | 1/26/2022, 9:49:34 AM |
| Library Catalog | www.degruyter.com |
| Language | en |
| 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. |
| Short Title | Bayesian approaches to variable selection |
| Date Added | 1/26/2022, 9:49:34 AM |
| Modified | 1/26/2022, 9:50:04 AM |
| Type | Journal Article |
|---|---|
| Author | Yusuke Yamaguchi |
| Author | Satoshi Yoshida |
| Author | Toshihiro Misumi |
| Author | Kazushi Maruo |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9315 |
| Volume | n/a |
| Issue | n/a |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2022 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9315 |
| DOI | 10.1002/sim.9315 |
| Accessed | 1/22/2022, 10:23:28 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 1/22/2022, 10:23:28 AM |
| Modified | 1/22/2022, 10:24:18 AM |
| Type | Journal Article |
|---|---|
| Author | Leah Isakov |
| Author | Andrew W. Lo |
| Author | Vahid Montazerhodjat |
| URL | https://www.sciencedirect.com/science/article/pii/S0304407618302380 |
| Series | Annals Issue in Honor of Jerry A. Hausman |
| Volume | 211 |
| Issue | 1 |
| Pages | 117-136 |
| Publication | Journal of Econometrics |
| ISSN | 0304-4076 |
| Date | July 1, 2019 |
| Journal Abbr | Journal of Econometrics |
| DOI | 10.1016/j.jeconom.2018.12.009 |
| Accessed | 1/20/2022, 5:58:27 AM |
| Library Catalog | ScienceDirect |
| Language | en |
| 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. |
| Short Title | Is the FDA too conservative or too aggressive? |
| Date Added | 1/20/2022, 5:58:27 AM |
| Modified | 1/20/2022, 5:59:22 AM |
| Type | Journal Article |
|---|---|
| Author | Chenyang Zhang |
| Author | Guosheng Yin |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13622 |
| Volume | n/a |
| Issue | n/a |
| Publication | Biometrics |
| ISSN | 1541-0420 |
| Date | 2022 |
| DOI | 10.1111/biom.13622 |
| Accessed | 1/17/2022, 7:37:08 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 1/17/2022, 7:39:27 AM |
| Modified | 1/19/2022, 7:46:02 AM |
| 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 |
| URL | https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.121.055393 |
| Volume | 144 |
| Issue | 4 |
| Pages | e70-e91 |
| Publication | Circulation |
| Date | July 27, 2021 |
| DOI | 10.1161/CIRCULATIONAHA.121.055393 |
| Accessed | 1/11/2022, 7:37:12 AM |
| Library Catalog | ahajournals.org (Atypon) |
| 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. |
| Short Title | Recommendations for Statistical Reporting in Cardiovascular Medicine |
| Date Added | 1/11/2022, 7:37:40 AM |
| Modified | 1/11/2022, 7:37:40 AM |
| 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 |
| URL | https://doi.org/10.1007/s43441-021-00357-x |
| Publication | Therapeutic Innovation & Regulatory Science |
| ISSN | 2168-4804 |
| Date | 2022-01-03 |
| Journal Abbr | Ther Innov Regul Sci |
| DOI | 10.1007/s43441-021-00357-x |
| Accessed | 1/8/2022, 7:51:22 AM |
| Library Catalog | Springer Link |
| Language | en |
| 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. |
| Short Title | Why are not There More Bayesian Clinical Trials? |
| Date Added | 1/8/2022, 7:51:55 AM |
| Modified | 1/8/2022, 7:51:55 AM |
| 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 |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2179 |
| Volume | n/a |
| Issue | n/a |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2179 |
| DOI | 10.1002/pst.2179 |
| Accessed | 12/15/2021, 8:31:12 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 12/15/2021, 8:31:12 PM |
| Modified | 12/15/2021, 8:32:07 PM |
| Type | Journal Article |
|---|---|
| Author | Fulvio De Santis |
| Author | Stefania Gubbiotti |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.202100035 |
| Volume | n/a |
| Issue | n/a |
| Publication | Biometrical Journal |
| ISSN | 1521-4036 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202100035 |
| DOI | 10.1002/bimj.202100035 |
| Accessed | 12/11/2021, 3:32:01 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 12/11/2021, 3:32:01 PM |
| Modified | 12/12/2021, 2:42:29 PM |
| Type | Journal Article |
|---|---|
| Author | Nikolaus Umlauf |
| Author | Nadja Klein |
| Author | Thorsten Simon |
| Author | Achim Zeileis |
| URL | https://www.jstatsoft.org/index.php/jss/article/view/v100i04 |
| Volume | 100 |
| Issue | 4 |
| Pages | 1 - 53 |
| Publication | Journal of Statistical Software |
| Date | November 30, 2021 |
| Extra | Section: Articles |
| Journal Abbr | J. Stat. Soft. |
| DOI | 10.18637/jss.v100.i04 |
| Accessed | 12/5/2021, 6:00:00 PM |
| 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 Added | 12/6/2021, 2:47:40 PM |
| Modified | 12/6/2021, 2:48:01 PM |
Shows linkage between optimization and sampling; uses optimization to start sampling
| 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 |
| URL | https://doi.org/10.1001/jama.2021.20929 |
| Publication | JAMA |
| ISSN | 0098-7484 |
| Date | November 30, 2021 |
| Journal Abbr | JAMA |
| DOI | 10.1001/jama.2021.20929 |
| Accessed | 12/2/2021, 11:20:41 AM |
| Library Catalog | Silverchair |
| 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 |
| Short Title | Effect of Intravenous or Intraosseous Calcium vs Saline on Return of Spontaneous Circulation in Adults With Out-of-Hospital Cardiac Arrest |
| Date Added | 12/2/2021, 11:20:41 AM |
| Modified | 12/2/2021, 11:21:29 AM |
Followed recently published Bayesian re-analysis reporting guideliness of Michael Harhay et al
| Type | Journal Article |
|---|---|
| Author | Thomas E. Bradstreet |
| URL | https://doi.org/10.1080/19466315.2020.1799856 |
| Volume | 13 |
| Issue | 3 |
| Pages | 344-354 |
| Publication | Statistics in Biopharmaceutical Research |
| ISSN | null |
| Date | July 3, 2021 |
| Extra | Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/19466315.2020.1799856 |
| DOI | 10.1080/19466315.2020.1799856 |
| Accessed | 11/12/2021, 1:45:29 PM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 11/12/2021, 1:45:29 PM |
| Modified | 11/12/2021, 1:45:50 PM |
| 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 |
| URL | https://doi.org/10.1007/s00134-021-06573-1 |
| Publication | Intensive Care Medicine |
| ISSN | 1432-1238 |
| Date | 2021-11-10 |
| Journal Abbr | Intensive Care Med |
| DOI | 10.1007/s00134-021-06573-1 |
| Accessed | 11/12/2021, 11:07:59 AM |
| Library Catalog | Springer Link |
| Language | en |
| 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. |
| Short Title | Dexamethasone 12 mg versus 6 mg for patients with COVID-19 and severe hypoxaemia |
| Date Added | 11/12/2021, 11:07:59 AM |
| Modified | 11/12/2021, 11:09:06 AM |
| Type | Journal Article |
|---|---|
| Author | Alberto Carmona-Bayonas |
| Author | Paula Jiménez-Fonseca |
| Author | Javier Gallego |
| Author | Pavlos Msaouel |
| Pages | 1-27 |
| Publication | Cancer Investigation |
| ISSN | 1532-4192 |
| Date | 2021-10-28 |
| Extra | PMID: 34709109 |
| Journal Abbr | Cancer Invest |
| DOI | 10.1080/07357907.2021.1999971 |
| Library Catalog | PubMed |
| Language | eng |
| 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 Added | 10/30/2021, 8:03:35 AM |
| Modified | 10/30/2021, 8:05:00 AM |
| Type | Journal Article |
|---|---|
| Author | David J. Lunn |
| Author | Jon Wakefield |
| Author | Amy Racine-Poon |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.922 |
| Volume | 20 |
| Issue | 15 |
| Pages | 2261-2285 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2001 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.922 |
| DOI | 10.1002/sim.922 |
| Accessed | 10/21/2021, 9:08:27 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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. |
| Short Title | Cumulative logit models for ordinal data |
| Date Added | 10/21/2021, 9:08:27 AM |
| Modified | 10/21/2021, 9:11:10 AM |
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.
| Type | Journal Article |
|---|---|
| Author | Junjing Lin |
| Author | Margaret Gamalo-Siebers |
| Author | Ram Tiwari |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2172 |
| Volume | n/a |
| Issue | n/a |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2172 |
| DOI | 10.1002/pst.2172 |
| Accessed | 9/30/2021, 11:15:42 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 9/30/2021, 11:15:42 AM |
| Modified | 9/30/2021, 11:16:11 AM |
| Type | Journal Article |
|---|---|
| Author | Paul Aubel |
| Author | Marine Antigny |
| Author | Ronan Fougeray |
| Author | Frédéric Dubois |
| Author | Gaëlle Saint-Hilary |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9186 |
| Volume | n/a |
| Issue | n/a |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9186 |
| DOI | 10.1002/sim.9186 |
| Accessed | 9/21/2021, 9:13:44 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 9/21/2021, 9:13:44 AM |
| Modified | 9/21/2021, 9:14:33 AM |
| 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 |
| URL | http://arxiv.org/abs/2011.01808 |
| Publication | arXiv:2011.01808 [stat] |
| Date | 2020-11-03 |
| Extra | arXiv: 2011.01808 |
| Accessed | 9/8/2021, 12:13:48 PM |
| Library Catalog | arXiv.org |
| 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 Added | 9/8/2021, 12:13:48 PM |
| Modified | 9/8/2021, 12:14:06 PM |
Comment: 77 pages, 35 figures
| Type | Journal Article |
|---|---|
| Author | Gisèle Nakhlé |
| Author | James M. Brophy |
| Author | Christel Renoux |
| Author | Paul Khairy |
| Author | Patrick Bélisle |
| Author | Jacques LeLorier |
| URL | https://www.jclinepi.com/article/S0895-4356(21)00282-1/abstract |
| Volume | 0 |
| Issue | 0 |
| Publication | Journal of Clinical Epidemiology |
| ISSN | 0895-4356, 1878-5921 |
| Date | 2021/09/07 |
| Extra | Publisher: Elsevier |
| Journal Abbr | Journal of Clinical Epidemiology |
| DOI | 10.1016/j.jclinepi.2021.09.002 |
| Accessed | 9/8/2021, 11:34:40 AM |
| Library Catalog | www.jclinepi.com |
| Language | English |
| 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]. |
| Short Title | Domperidone Increases Harmful Cardiac Events in Parkinson's Disease |
| Date Added | 9/8/2021, 11:34:40 AM |
| Modified | 9/8/2021, 11:35:12 AM |
| Type | Journal Article |
|---|---|
| Author | ATTACC Investigators |
| URL | https://doi.org/10.1056/NEJMoa2105911 |
| Volume | 0 |
| Issue | 0 |
| Pages | null |
| Publication | New England Journal of Medicine |
| ISSN | 0028-4793 |
| Date | August 4, 2021 |
| Extra | Publisher: Massachusetts Medical Society _eprint: https://doi.org/10.1056/NEJMoa2105911 |
| DOI | 10.1056/NEJMoa2105911 |
| Accessed | 8/7/2021, 2:31:29 PM |
| Library Catalog | Taylor and Francis+NEJM |
| Date Added | 8/7/2021, 2:31:29 PM |
| Modified | 8/7/2021, 2:33:06 PM |
| Type | Journal Article |
|---|---|
| Author | Frank E. Harrell |
| Author | Tina Shih |
| Volume | 17 |
| Pages | 17-26 |
| Publication | Int J Tech Assess Hlth Care |
| Date | 2001 |
| Extra | Citation Key: har01usi tex.citeulike-article-id= 13265138 tex.posted-at= 2014-07-14 14:09:50 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 8/5/2021, 11:12:37 AM |
| Type | Journal Article |
|---|---|
| Author | Niko A. Kaciroti |
| Author | Roderick J. A. Little |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9083 |
| Rights | © 2021 John Wiley & Sons Ltd. |
| Volume | n/a |
| Issue | n/a |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9083 |
| DOI | https://doi.org/10.1002/sim.9083 |
| Accessed | 6/3/2021, 10:26:33 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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. |
| Short Title | Bayesian sensitivity analyses for longitudinal data with dropouts that are potentially missing not at random |
| Date Added | 6/3/2021, 10:26:33 AM |
| Modified | 6/3/2021, 10:27:26 AM |
| 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 |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9021 |
| Rights | © 2021 John Wiley & Sons, Ltd. |
| Volume | n/a |
| Issue | n/a |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9021 |
| DOI | https://doi.org/10.1002/sim.9021 |
| Accessed | 5/8/2021, 8:36:35 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 5/8/2021, 8:36:35 AM |
| Modified | 5/8/2021, 8:37:48 AM |
| 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 |
| URL | https://doi.org/10.1080/00031305.2021.1901782 |
| Volume | 0 |
| Issue | 0 |
| Pages | 1-9 |
| Publication | The American Statistician |
| ISSN | 0003-1305 |
| Date | March 22, 2021 |
| Extra | Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00031305.2021.1901782 |
| DOI | 10.1080/00031305.2021.1901782 |
| Accessed | 4/23/2021, 10:59:37 AM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 4/23/2021, 10:59:37 AM |
| Modified | 4/23/2021, 11:00:06 AM |
| 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 |
| URL | https://www.liebertpub.com/doi/10.1089/pancan.2020.0021 |
| Volume | 7 |
| Issue | 1 |
| Pages | 8-19 |
| Publication | Journal of Pancreatic Cancer |
| Date | March 1, 2021 |
| Extra | Publisher: Mary Ann Liebert, Inc., publishers |
| DOI | 10.1089/pancan.2020.0021 |
| Accessed | 3/30/2021, 7:26:13 AM |
| Library Catalog | liebertpub.com (Atypon) |
| 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 Added | 3/30/2021, 7:26:13 AM |
| Modified | 4/4/2021, 8:21:16 AM |
| Type | Journal Article |
|---|---|
| Author | Ofir Harari |
| Author | Grace Hsu |
| Author | Louis Dron |
| Author | Jay J. H. Park |
| Author | Kristian Thorlund |
| Author | Edward J. Mills |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2073 |
| Rights | © 2020 John Wiley & Sons Ltd |
| Volume | 20 |
| Issue | 2 |
| Pages | 256-271 |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2073 |
| DOI | https://doi.org/10.1002/pst.2073 |
| Accessed | 3/7/2021, 9:50:48 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 3/7/2021, 9:50:48 AM |
| Modified | 3/7/2021, 9:51:20 AM |
| 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 |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2072 |
| Rights | © 2020 John Wiley & Sons Ltd |
| Volume | 20 |
| Issue | 2 |
| Pages | 245-255 |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2072 |
| DOI | https://doi.org/10.1002/pst.2072 |
| Accessed | 3/7/2021, 9:40:41 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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. |
| Short Title | The current state of Bayesian methods in nonclinical pharmaceutical statistics |
| Date Added | 3/7/2021, 9:40:41 AM |
| Modified | 3/7/2021, 9:41:31 AM |
| Type | Journal Article |
|---|---|
| Author | Fernando G. Zampieri |
| Author | Jonathan D. Casey |
| Author | Manu Shankar-Hari |
| Author | Frank E. Harrell |
| Author | Michael O. Harhay |
| URL | https://www.atsjournals.org/doi/10.1164/rccm.202006-2381CP |
| Volume | 203 |
| Issue | 5 |
| Pages | 543-552 |
| Publication | American Journal of Respiratory and Critical Care Medicine |
| ISSN | 1073-449X |
| Date | December 3, 2020 |
| Extra | Publisher: American Thoracic Society - AJRCCM |
| Journal Abbr | Am J Respir Crit Care Med |
| DOI | 10.1164/rccm.202006-2381CP |
| Accessed | 3/1/2021, 2:54:29 PM |
| Library Catalog | atsjournals.org (Atypon) |
| 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 Added | 3/1/2021, 2:54:29 PM |
| Modified | 3/1/2021, 2:55:01 PM |
| Type | Journal Article |
|---|---|
| Author | REMAP-CAP Investigators |
| URL | https://dx.doi.org/10.1056/nejmoa2100433 |
| Publication | New England Journal of Medicine |
| ISSN | 0028-4793 |
| Date | 2021 |
| Extra | Publisher: Massachusetts Medical Society |
| Journal Abbr | New England Journal of Medicine |
| DOI | 10.1056/nejmoa2100433 |
| Date Added | 2/27/2021, 7:28:40 AM |
| Modified | 2/27/2021, 7:30:16 AM |
| Type | Journal Article |
|---|---|
| Author | Sama Shrestha |
| Author | Sonia Jain |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8873 |
| Rights | © 2021 John Wiley & Sons, Ltd. |
| Volume | n/a |
| Issue | n/a |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.8873 |
| DOI | https://doi.org/10.1002/sim.8873 |
| Accessed | 1/30/2021, 3:00:53 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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. |
| Date Added | 1/30/2021, 3:00:53 PM |
| Modified | 1/30/2021, 3:01:29 PM |
| Type | Journal Article |
|---|---|
| Author | Noorian, Sajad |
| Author | Ganjali, Mojtaba |
| URL | https://www.academia.edu/30796618/Bayesian_Analysis_of_Transition_Model_for_Longitudinal_Ordinal_Response_Data_Application_to_Insomnia_Data |
| Volume | 1 |
| Issue | 2 |
| Pages | 148-161 |
| Publication | International Journal of Statistics in Medical Research |
| ISSN | 1929-6029 |
| Date | 2012 |
| Accessed | 12/22/2020, 8:21:14 AM |
| Library Catalog | www.academia.edu |
| Language | en |
| Abstract | Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data: Application to Insomnia Data |
| Short Title | Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data |
| Date Added | 12/22/2020, 8:21:14 AM |
| Modified | 1/24/2021, 7:21:47 AM |
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.
| Type | Journal Article |
|---|---|
| Author | Sarah Depaoli |
| Author | Rens van de Schoot |
| Volume | 22 |
| Issue | 2 |
| Pages | 240-261 |
| Publication | Psychological Methods |
| ISSN | 1939-1463 |
| Date | 2017-06 |
| Extra | PMID: 26690773 |
| Journal Abbr | Psychol Methods |
| DOI | 10.1037/met0000065 |
| Library Catalog | PubMed |
| Language | eng |
| 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 |
| Short Title | Improving transparency and replication in Bayesian statistics |
| Date Added | 1/21/2021, 5:16:22 PM |
| Modified | 1/21/2021, 5:17:12 PM |
| Type | Journal Article |
|---|---|
| Author | George Casella |
| Author | Edward I. George |
| Volume | 46 |
| Pages | 167-174 |
| Publication | Am Statistician |
| Date | 1992 |
| Extra | Citation Key: cas92exp tex.citeulike-article-id= 13263865 tex.posted-at= 2014-07-14 14:09:24 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 1/19/2021, 8:04:49 AM |
| Type | Book |
|---|---|
| Author | Peter F. Thall |
| URL | https://www.springer.com/gp/book/9783030437138 |
| Series | Springer Series in Pharmaceutical Statistics |
| Publisher | Springer International Publishing |
| ISBN | 978-3-030-43713-8 |
| Date | 2020 |
| Extra | DOI: 10.1007/978-3-030-43714-5 |
| Accessed | 1/9/2021, 7:48:50 AM |
| Library Catalog | www.springer.com |
| Language | en |
| 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 Added | 1/9/2021, 7:48:50 AM |
| Modified | 1/9/2021, 7:50:00 AM |
| Type | Journal Article |
|---|---|
| Author | David A. Harville |
| URL | https://doi.org/10.1080/00031305.2020.1858963 |
| Series | mv Ba |
| Volume | 0 |
| Issue | 0 |
| Pages | 1-7 |
| Publication | The American Statistician |
| ISSN | 0003-1305 |
| Date | 2021 |
| Extra | Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00031305.2020.1858963 |
| DOI | 10.1080/00031305.2020.1858963 |
| Accessed | 1/6/2021, 11:05:03 AM |
| Library Catalog | Taylor and Francis+NEJM |
| 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. |
| Short Title | Bayesian Inference Is Unaffected by Selection |
| Date Added | 1/6/2021, 11:05:03 AM |
| Modified | 1/6/2021, 11:19:49 AM |
| Type | Journal Article |
|---|---|
| Author | David Rindskopf |
| URL | https://doi.org/10.1177/0193841X20977619 |
| Pages | 0193841X20977619 |
| Publication | Evaluation Review |
| ISSN | 0193-841X |
| Date | December 30, 2020 |
| Extra | Publisher: SAGE Publications Inc |
| Journal Abbr | Eval Rev |
| DOI | 10.1177/0193841X20977619 |
| Accessed | 1/5/2021, 9:15:07 AM |
| Library Catalog | SAGE Journals |
| Language | en |
| 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 Added | 1/5/2021, 9:15:07 AM |
| Modified | 1/5/2021, 9:15:56 AM |
| Type | Journal Article |
|---|---|
| Author | Trung Dung Tran |
| Author | Emmanuel Lesaffre |
| Author | Geert Verbeke |
| Author | Joke Duyck |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13292 |
| Rights | © 2020 The International Biometric Society |
| Volume | n/a |
| Issue | n/a |
| Publication | Biometrics |
| ISSN | 1541-0420 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13292 |
| DOI | https://doi.org/10.1111/biom.13292 |
| Accessed | 12/11/2020, 4:05:37 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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. |
| Date Added | 12/11/2020, 4:05:37 PM |
| Modified | 12/11/2020, 4:06:14 PM |
| Type | Journal Article |
|---|---|
| Author | Gao Wang |
| Author | Abhishek Sarkar |
| Author | Peter Carbonetto |
| Author | Matthew Stephens |
| URL | https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssb.12388 |
| 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. |
| Volume | 82 |
| Issue | 5 |
| Pages | 1273-1300 |
| Publication | Journal of the Royal Statistical Society: Series B (Statistical Methodology) |
| ISSN | 1467-9868 |
| Date | 2020 |
| Extra | _eprint: https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/rssb.12388 |
| DOI | https://doi.org/10.1111/rssb.12388 |
| Accessed | 12/8/2020, 1:53:12 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 12/8/2020, 1:53:12 PM |
| Modified | 12/8/2020, 1:54:02 PM |
| Type | Journal Article |
|---|---|
| Author | Tony O'Hagan |
| URL | https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.1740-9713.2004.00050.x |
| Rights | © 2004 The Royal Statistical Society |
| Volume | 1 |
| Issue | 3 |
| Pages | 132-133 |
| Publication | Significance |
| ISSN | 1740-9713 |
| Date | 2004 |
| Extra | _eprint: https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1740-9713.2004.00050.x |
| DOI | https://doi.org/10.1111/j.1740-9713.2004.00050.x |
| Accessed | 11/22/2020, 3:49:11 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 11/22/2020, 3:49:11 PM |
| Modified | 11/22/2020, 3:49:53 PM |
| Type | Journal Article |
|---|---|
| Author | Richard Simon |
| Author | Laurence S. Freedman |
| Volume | 53 |
| Pages | 456-464 |
| Publication | Biometrics |
| Date | 1997 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/15/2020, 12:26:45 PM |
| Type | Journal Article |
|---|---|
| Author | Constantin Volkmann |
| Author | Alexander Volkmann |
| Author | Christian A. Müller |
| URL | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241497 |
| Volume | 15 |
| Issue | 11 |
| Pages | e0241497 |
| Publication | PLOS ONE |
| ISSN | 1932-6203 |
| Date | Nov 11, 2020 |
| Extra | Publisher: Public Library of Science |
| Journal Abbr | PLOS ONE |
| DOI | 10.1371/journal.pone.0241497 |
| Accessed | 11/11/2020, 3:00:03 PM |
| Library Catalog | PLoS Journals |
| Language | en |
| 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. |
| Short Title | On the treatment effect heterogeneity of antidepressants in major depression |
| Date Added | 11/11/2020, 3:00:06 PM |
| Modified | 11/11/2020, 3:00:48 PM |
| Type | Journal Article |
|---|---|
| Author | Ofir Harari |
| Author | Grace Hsu |
| Author | Louis Dron |
| Author | Jay J. H. Park |
| Author | Kristian Thorlund |
| Author | Edward J. Mills |
| URL | https://dx.doi.org/10.1002/pst.2073 |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1604 |
| Date | 2020 |
| Extra | Publisher: Wiley |
| Journal Abbr | Pharmaceutical Statistics |
| DOI | 10.1002/pst.2073 |
| Date Added | 10/30/2020, 9:10:04 AM |
| Modified | 10/30/2020, 9:10:44 AM |
| Type | Journal Article |
|---|---|
| Author | Ofir Harari |
| Author | Grace Hsu |
| Author | Louis Dron |
| Author | Jay J. H. Park |
| Author | Kristian Thorlund |
| Author | Edward J. Mills |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2073 |
| Rights | © 2020 John Wiley & Sons Ltd |
| Volume | n/a |
| Issue | n/a |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2020 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2073 |
| DOI | 10.1002/pst.2073 |
| Accessed | 10/10/2020, 11:03:58 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 10/10/2020, 11:03:58 AM |
| Modified | 10/10/2020, 11:04:56 AM |
| Type | Journal Article |
|---|---|
| Author | Alex Karanevich |
| Author | Richard Meier |
| Author | Stefan Graw |
| Author | Anna McGlothlin |
| Author | Byron Gajewski |
| URL | https://doi.org/10.1080/00031305.2019.1610065 |
| Volume | 0 |
| Issue | 0 |
| Pages | 1-10 |
| Publication | The American Statistician |
| ISSN | 0003-1305 |
| Date | May 3, 2019 |
| Extra | Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00031305.2019.1610065 |
| DOI | 10.1080/00031305.2019.1610065 |
| Accessed | 9/20/2020, 10:37:25 AM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 9/20/2020, 10:37:25 AM |
| Modified | 9/20/2020, 10:39:09 AM |
| Type | Journal Article |
|---|---|
| Author | Boxian Wei |
| Author | Thomas M. Braun |
| Author | Roy N. Tamura |
| Author | Kelley Kidwell |
| URL | https://doi.org/10.1080/10543406.2020.1815032 |
| Volume | 0 |
| Issue | 0 |
| Pages | 1-12 |
| Publication | Journal of Biopharmaceutical Statistics |
| ISSN | 1054-3406 |
| Date | September 6, 2020 |
| Extra | Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10543406.2020.1815032 PMID: 32892710 |
| DOI | 10.1080/10543406.2020.1815032 |
| Accessed | 9/12/2020, 2:01:11 PM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 9/12/2020, 2:01:11 PM |
| Modified | 9/12/2020, 2:01:52 PM |
| Type | Journal Article |
|---|---|
| Author | Spencer Woody |
| Author | Carlos M. Carvalho |
| Author | Jared S. Murray |
| URL | https://doi.org/10.1080/10618600.2020.1796684 |
| Volume | 0 |
| Issue | 0 |
| Pages | 1-9 |
| Publication | Journal of Computational and Graphical Statistics |
| ISSN | 1061-8600 |
| Date | July 21, 2020 |
| Extra | Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10618600.2020.1796684 |
| DOI | 10.1080/10618600.2020.1796684 |
| Accessed | 8/26/2020, 4:40:14 PM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 8/26/2020, 4:40:17 PM |
| Modified | 8/26/2020, 4:40:54 PM |
| Type | Journal Article |
|---|---|
| Author | Stephen J. Ruberg |
| URL | https://ascpt.onlinelibrary.wiley.com/doi/abs/10.1002/cpt.2004 |
| Rights | This article is protected by copyright. All rights reserved. |
| Volume | n/a |
| Issue | n/a |
| Publication | Clinical Pharmacology & Therapeutics |
| ISSN | 1532-6535 |
| Date | 2020 |
| Extra | _eprint: https://ascpt.onlinelibrary.wiley.com/doi/pdf/10.1002/cpt.2004 |
| DOI | 10.1002/cpt.2004 |
| Accessed | 8/6/2020, 8:31:38 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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. |
| Short Title | Détente |
| Date Added | 8/6/2020, 8:31:39 AM |
| Modified | 8/6/2020, 8:32:23 AM |
| Type | Journal Article |
|---|---|
| Author | Stan Development Team |
| URL | https://cran.r-project.org/package=rstan |
| Date | 2020 |
| 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 |
| Type | Journal Article |
|---|---|
| Author | Liyun Jiang |
| Author | Fangrong Yan |
| Author | Peter F. Thall |
| Author | Xuelin Huang |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2046 |
| Rights | © 2020 John Wiley & Sons Ltd |
| Volume | n/a |
| Issue | n/a |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2020 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2046 |
| DOI | 10.1002/pst.2046 |
| Accessed | 8/2/2020, 7:19:31 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 8/2/2020, 7:19:31 AM |
| Modified | 8/2/2020, 7:20:50 AM |
| Type | Journal Article |
|---|---|
| Author | Clare Marshall |
| Author | Nicky Best |
| Author | Alex Bottle |
| Author | Paul Aylin |
| URL | https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-985X.2004.apm10.x |
| Volume | 167 |
| Issue | 3 |
| Pages | 541-559 |
| Publication | Journal of the Royal Statistical Society: Series A (Statistics in Society) |
| ISSN | 1467-985X |
| Date | 2004 |
| Extra | _eprint: https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-985X.2004.apm10.x |
| DOI | 10.1111/j.1467-985X.2004.apm10.x |
| Accessed | 7/8/2020, 5:55:40 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 7/8/2020, 5:55:40 AM |
| Modified | 7/8/2020, 5:57:02 AM |
| Type | Web Page |
|---|---|
| Author | Jingjing Ye |
| Author | Gregory Reaman |
| Author | R Angelo De Claro |
| Author | Rajeshwari Sridhara |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2039?af=R |
| Date | 2020 |
| Accessed | 6/21/2020, 6:42:08 AM |
| 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 Added | 6/21/2020, 6:42:08 AM |
| Modified | 6/21/2020, 6:50:15 AM |
| Type | Journal Article |
|---|---|
| Author | Yan-Cheng Chao |
| Author | Thomas M. Braun |
| Author | Roy N. Tamura |
| Author | Kelley M. Kidwell |
| URL | https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssc.12406 |
| Rights | © 2020 Royal Statistical Society |
| Volume | n/a |
| Issue | n/a |
| Publication | Journal of the Royal Statistical Society: Series C (Applied Statistics) |
| ISSN | 1467-9876 |
| Date | 2020 |
| Extra | _eprint: https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/rssc.12406 |
| DOI | 10.1111/rssc.12406 |
| Accessed | 4/12/2020, 11:23:48 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 4/12/2020, 11:23:48 AM |
| Modified | 4/12/2020, 11:24:44 AM |
| Type | Journal Article |
|---|---|
| Author | Marcus Bendtsen |
| URL | https://www.jmir.org/2018/10/e10873/ |
| Volume | 20 |
| Issue | 10 |
| Pages | e10873 |
| Publication | Journal of Medical Internet Research |
| Date | 2018 |
| 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 |
| DOI | 10.2196/10873 |
| Accessed | 3/16/2020, 3:09:03 PM |
| Library Catalog | www.jmir.org |
| Language | en |
| 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] |
| Short Title | A Gentle Introduction to the Comparison Between Null Hypothesis Testing and Bayesian Analysis |
| Date Added | 3/16/2020, 3:09:03 PM |
| Modified | 3/16/2020, 3:09:43 PM |
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.
| Type | Journal Article |
|---|---|
| Author | Beat Neuenschwander |
| Author | Sebastian Weber |
| Author | Heinz Schmidli |
| Author | Anthony O'Hagan |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13252 |
| Rights | This article is protected by copyright. All rights reserved |
| Volume | n/a |
| Issue | n/a |
| Publication | Biometrics |
| ISSN | 1541-0420 |
| Date | 2020 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13252 |
| DOI | 10.1111/biom.13252 |
| Accessed | 3/7/2020, 6:21:14 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 3/7/2020, 6:21:14 AM |
| Modified | 3/7/2020, 6:21:57 AM |
| Type | Journal Article |
|---|---|
| Author | Donald A. Berry |
| URL | https://projecteuclid.org/euclid.ss/1089808281 |
| Volume | 19 |
| Issue | 1 |
| Pages | 175-187 |
| Publication | Statistical Science |
| ISSN | 0883-4237, 2168-8745 |
| Date | 2004-02 |
| Extra | MR: MR2086326 Zbl: 1057.62096 |
| Journal Abbr | Statist. Sci. |
| DOI | 10.1214/088342304000000044 |
| Accessed | 12/21/2019, 8:11:06 AM |
| Library Catalog | Project Euclid |
| Language | en |
| 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 Added | 12/21/2019, 8:11:06 AM |
| Modified | 12/21/2019, 8:11:46 AM |
| Type | Journal Article |
|---|---|
| Author | John E. Brush |
| Author | Mark Lee |
| Author | Jonathan Sherbino |
| Author | Judith C. Taylor-Fishwick |
| Author | Geoffrey Norman |
| URL | https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2757877 |
| Volume | 2 |
| Issue | 12 |
| Pages | e1918023-e1918023 |
| Publication | JAMA Network Open |
| Date | 2019/12/02 |
| Journal Abbr | JAMA Netw Open |
| DOI | 10.1001/jamanetworkopen.2019.18023 |
| Accessed | 12/21/2019, 7:23:26 AM |
| Library Catalog | jamanetwork.com |
| Language | en |
| 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> < .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> |
| 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 |
| Date Added | 12/21/2019, 7:23:26 AM |
| Modified | 12/21/2019, 7:23:56 AM |
| Type | Journal Article |
|---|---|
| Author | Yi Liu |
| Author | Feng Guo |
| URL | https://amstat.tandfonline.com/doi/abs/10.1080/10618600.2019.1686988 |
| Pages | 1-12 |
| Publication | Journal of Computational and Graphical Statistics |
| ISSN | 1061-8600 |
| Date | October 31, 2019 |
| Journal Abbr | Journal of Computational and Graphical Statistics |
| DOI | 10.1080/10618600.2019.1686988 |
| Accessed | 12/16/2019, 7:59:14 AM |
| Library Catalog | amstat.tandfonline.com (Atypon) |
| 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 Added | 12/16/2019, 7:59:14 AM |
| Modified | 12/16/2019, 7:59:42 AM |
| Type | Journal Article |
|---|---|
| Author | Steffen Ventz |
| Author | Matteo Cellamare |
| Author | Sergio Bacallado |
| Author | Lorenzo Trippa |
| URL | https://doi.org/10.1080/01621459.2018.1497497 |
| Volume | 114 |
| Issue | 527 |
| Pages | 962-974 |
| Publication | Journal of the American Statistical Association |
| ISSN | 0162-1459 |
| Date | July 3, 2019 |
| DOI | 10.1080/01621459.2018.1497497 |
| Accessed | 12/16/2019, 7:52:46 AM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 12/16/2019, 7:52:46 AM |
| Modified | 12/16/2019, 7:53:29 AM |
| Type | Journal Article |
|---|---|
| Author | Andrew P. Grieve |
| URL | www.jstor.org/stable/2337407 |
| Volume | 82 |
| Issue | 2 |
| Pages | 277-286 |
| Publication | Biometrika |
| ISSN | 0006-3444 |
| Date | 1995 |
| DOI | 10.2307/2337407 |
| Accessed | 12/7/2019, 6:09:06 PM |
| Library Catalog | JSTOR |
| 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. |
| Archive | JSTOR |
| Date Added | 12/7/2019, 6:09:06 PM |
| Modified | 12/7/2019, 6:10:07 PM |
| Type | Journal Article |
|---|---|
| Author | A. P. Grieve |
| URL | www.jstor.org/stable/2530969 |
| Volume | 41 |
| Issue | 4 |
| Pages | 979-990 |
| Publication | Biometrics |
| ISSN | 0006-341X |
| Date | 1985 |
| DOI | 10.2307/2530969 |
| Accessed | 12/7/2019, 6:05:32 PM |
| Library Catalog | JSTOR |
| 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. |
| Archive | JSTOR |
| Date Added | 12/7/2019, 6:05:33 PM |
| Modified | 12/7/2019, 6:06:14 PM |
| 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 |
| URL | https://doi.org/10.1056/NEJMoa1905795 |
| Volume | 381 |
| Issue | 22 |
| Pages | 2103-2113 |
| Publication | New England Journal of Medicine |
| ISSN | 0028-4793 |
| Date | November 28, 2019 |
| DOI | 10.1056/NEJMoa1905795 |
| Accessed | 11/29/2019, 2:26:32 PM |
| Library Catalog | Taylor and Francis+NEJM |
| Date Added | 11/29/2019, 2:26:32 PM |
| Modified | 11/29/2019, 2:27:12 PM |
| Type | Journal Article |
|---|---|
| Author | Amy LaLonde |
| Author | Tanzy Love |
| Author | Sally W. Thurston |
| Author | Philip W. Davidson |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13174 |
| Rights | © 2019 The International Biometric Society |
| Volume | n/a |
| Issue | n/a |
| Publication | Biometrics |
| ISSN | 1541-0420 |
| Date | 2020 |
| DOI | 10.1111/biom.13174 |
| Accessed | 11/29/2019, 2:24:03 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 11/29/2019, 2:24:03 PM |
| Modified | 11/29/2019, 2:24:58 PM |
| Type | Journal Article |
|---|---|
| Author | Corwin Matthew Zigler |
| URL | https://doi.org/10.1080/00031305.2015.1111260 |
| Volume | 70 |
| Issue | 1 |
| Pages | 47-54 |
| Publication | The American Statistician |
| ISSN | 0003-1305 |
| Date | January 2, 2016 |
| Extra | PMID: 27482121 |
| DOI | 10.1080/00031305.2015.1111260 |
| Accessed | 11/25/2019, 7:36:13 AM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 11/25/2019, 7:36:13 AM |
| Modified | 11/25/2019, 7:37:12 AM |
| Type | Journal Article |
|---|---|
| Author | Paola Sebastiani |
| Author | Emanuela Gussoni |
| Author | Isaac S. Kohane |
| Author | Marco F. Ramoni |
| Volume | 18 |
| Pages | 33-70 |
| Publication | Stat Sci |
| Date | 2003 |
| Extra | Citation Key: seb03sta tex.citeulike-article-id= 13265349 tex.posted-at= 2014-07-14 14:09:54 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
excellent overview of genetics, DNA, microarray; other interesting articles follow
| Type | Journal Article |
|---|---|
| Author | T. Härkänen |
| Author | P. Knekt |
| Author | E. Virtala |
| Author | O. Lindfors |
| Author | The Helsinki Psychotherapy Study Group |
| Volume | 24 |
| Pages | 3773-3787 |
| Publication | Stat Med |
| Date | 2005 |
| Extra | Citation Key: har05cas tex.citeulike-article-id= 13265454 tex.posted-at= 2014-07-14 14:09:57 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
use of surrogate data collected during interviews of patients who dropped out
| Type | Journal Article |
|---|---|
| Author | Donald A. Berry |
| Volume | 5 |
| Pages | 27-36 |
| Publication | Nat Rev |
| 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 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
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."
| Type | Journal Article |
|---|---|
| Author | Ofer Harel |
| Author | Xiao-Hua Zhou |
| Volume | 26 |
| Pages | 3769-3786 |
| Publication | Stat Med |
| Date | 2006 |
| Extra | Citation Key: har06mul tex.citeulike-article-id= 13265543 tex.posted-at= 2014-07-14 14:09:58 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
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
| Type | Journal Article |
|---|---|
| Author | Shelley B. Bull |
| Author | Juan P. Lewinger |
| Author | Sophia S. F. Lee |
| Volume | 26 |
| Pages | 903-918 |
| Publication | Stat Med |
| Date | 2007 |
| Extra | Citation Key: bul07con tex.citeulike-article-id= 13265558 tex.posted-at= 2014-07-14 14:09:59 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
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
| Type | Journal Article |
|---|---|
| Author | Tilmann Gneiting |
| Author | Adrian E. Raftery |
| Volume | 102 |
| Pages | 359-378 |
| Publication | J Am Stat Assoc |
| Date | 2007 |
| Extra | Citation Key: gne07str tex.citeulike-article-id= 13265560 tex.posted-at= 2014-07-14 14:09:59 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
wonderful review article except missing references from Scandanavian and German medical decision making literature
| Type | Journal Article |
|---|---|
| Author | Lawrence C. McCandless |
| Author | Paul Gustafson |
| Author | Peter C. Austin |
| Volume | 28 |
| Pages | 94-112 |
| Publication | Stat Med |
| Date | 2009 |
| Extra | Citation Key: mcc09bay tex.citeulike-article-id= 13265723 tex.posted-at= 2014-07-14 14:10:02 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
using Bayesian credible intervals to adjust for uncertainty in estimation of propensity score;relied heavily on Rubin 5-category propensity adjustment
| Type | Journal Article |
|---|---|
| Author | Melissa Spann |
| Author | Stacy Lindborg |
| Author | John Seaman |
| Author | Robert Baker |
| Author | Eduardo Dunayevich |
| Author | Alan Breier |
| URL | http://dx.doi.org/10.1016/j.jpsychires.2008.07.009 |
| Volume | 43 |
| Pages | 561-567 |
| Publication | J Psych Res |
| Date | 2009 |
| 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 |
| 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 |
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
| Type | Journal Article |
|---|---|
| Author | Duminda N. Wijeysundera |
| Author | Peter C. Austin |
| Author | Janet E. Hux |
| Author | W. Scott Beattie |
| Author | Andreas Laupacis |
| Volume | 62 |
| Pages | 13-21 |
| Publication | J Clin Epi |
| Date | 2009 |
| Extra | Citation Key: wij09bay tex.citeulike-article-id= 13265722 tex.posted-at= 2014-07-14 14:10:02 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
Bayesian re-analysis of trials analyzed using frequentist methods
| Type | Book |
|---|---|
| Author | V. Barnett |
| Edition | Second |
| Publisher | Wiley |
| Date | 1982 |
| Extra | Citation Key: bar82com tex.citeulike-article-id= 13263727 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
This is a nice comparative account of frequentist and Bayesian methods. In particular, it contains a chapter on the various approaches to prior specification
| Type | Journal Article |
|---|---|
| Author | J. Berger |
| Author | T. Sellke |
| Volume | 82 |
| Pages | 112-139 |
| Publication | J Am Stat Assoc |
| Date | 1987 |
| Extra | Citation Key: ber87tes tex.citeulike-article-id= 13263747 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | James O. Berger |
| Author | Donald A. Berry |
| Volume | 76 |
| Pages | 159-165 |
| Publication | Am Scientist |
| Date | 1988 |
| Extra | Citation Key: ber88sta tex.citeulike-article-id= 13263749 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Edward J. Bedrick |
| Author | Ronald Christensen |
| Author | Wesley Johnson |
| Volume | 91 |
| Pages | 1450-1460 |
| Publication | J Am Stat Assoc |
| Date | 1996 |
| Extra | Citation Key: bed96new tex.citeulike-article-id= 13263734 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Edward J. Bedrick |
| Author | Ronald Christensen |
| Author | Wesley Johnson |
| Volume | 51 |
| Pages | 211-218 |
| Publication | Am Statistician |
| Date | 1997 |
| Extra | Citation Key: bed97bay tex.citeulike-article-id= 13263735 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Donald A. Berry |
| URL | http://dx.doi.org/10.1080/00031305.1987.10475458 |
| Volume | 41 |
| Pages | 117-122 |
| Publication | Am Statistician |
| Date | 1987 |
| 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 |
| DOI | 10.1080/00031305.1987.10475458 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Donald A. Berry |
| Volume | 51 |
| Pages | 241-246 |
| Publication | Am Statistician |
| Date | 1997 |
| Extra | Citation Key: ber97tea tex.citeulike-article-id= 13263759 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | N. G. Best |
| Author | D. J. Spiegelhalter |
| Author | A. Thomas |
| Author | C. E. G. Brayne |
| Volume | 159 |
| Pages | 323-342 |
| Publication | J Roy Stat Soc A |
| Date | 1996 |
| Extra | Citation Key: bes96bay tex.citeulike-article-id= 13263761 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Donald A. Berry |
| Author | Mark C. Wolff |
| Author | David Sack |
| URL | http://dx.doi.org/10.1016/0197-2456(94)90033-7 |
| Volume | 15 |
| Pages | 360-378 |
| Publication | Controlled Clin Trials |
| Date | 1994 |
| 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 |
| 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 |
| Type | Journal Article |
|---|---|
| Author | J. O. Berger |
| Author | B. Boukai |
| Author | Y. Wang |
| Volume | 12 |
| Pages | 133-160 |
| Publication | Stat Sci |
| Date | 1997 |
| Extra | Citation Key: ber97uni tex.citeulike-article-id= 13263760 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
"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
| Type | Journal Article |
|---|---|
| Author | Ken P. Kleinman |
| Author | Joseph G. Ibrahim |
| Volume | 54 |
| Pages | 921-938 |
| Publication | Biometrics |
| Date | 1998 |
| Extra | Citation Key: ble98sem tex.citeulike-article-id= 13263776 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Norman Breslow |
| Volume | 5 |
| Pages | 269-298 |
| Publication | Stat Sci |
| Date | 1990 |
| Extra | Citation Key: bre90bio tex.citeulike-article-id= 13263806 tex.posted-at= 2014-07-14 14:09:23 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | James M. Brophy |
| Author | Lawrence Joseph |
| Volume | 273 |
| Pages | 871-875 |
| Publication | JAMA |
| Date | 1995 |
| Extra | Citation Key: bro95pla tex.citeulike-article-id= 13263819 tex.posted-at= 2014-07-14 14:09:23 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Lyle D. Broemeling |
| Author | Peyton Cook |
| Volume | 16 |
| Pages | 321-332 |
| Publication | Stat Med |
| Date | 1997 |
| Extra | Citation Key: bro97bay tex.citeulike-article-id= 13263820 tex.posted-at= 2014-07-14 14:09:23 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| 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 |
| URL | http://www.mrc-bsu.cam.ac.uk/bugs |
| Volume | 4 |
| Place | Oxford, UK |
| Publisher | Clarendon Press |
| Pages | 837-842 |
| Date | 1992 |
| 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 |
| Book Title | Bayesian Statistics |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | W. R. Gilks |
| Author | A. Thomas |
| Author | D. J. Spiegelhalter |
| URL | http://www.mrc-bsu.cam.ac.uk/bugs |
| Volume | 43 |
| Pages | 169-177 |
| Publication | The Statistician |
| Date | 1994 |
| 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 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | A. E. Raftery |
| Volume | 83 |
| Pages | 251-266 |
| Publication | Biometrika |
| Date | 1996 |
| Extra | Citation Key: raf96app tex.citeulike-article-id= 13264721 tex.posted-at= 2014-07-14 14:09:40 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Trivellore E. Raghunathan |
| Author | David S. Siscovick |
| Volume | 45 |
| Pages | 335-352 |
| Publication | Appl Stat |
| Date | 1996 |
| Extra | Citation Key: rag96mul tex.citeulike-article-id= 13264723 tex.posted-at= 2014-07-14 14:09:40 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Book Section |
|---|---|
| Author | J. Qian |
| Author | D. K. Stangl |
| Author | S. L. George |
| Place | New York |
| Publisher | Marcel Dekker |
| Pages | 187-215 |
| Date | 1996 |
| Extra | Citation Key: qia96wei tex.citeulike-article-id= 13264714 tex.posted-at= 2014-07-14 14:09:40 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Gillian M. Raab |
| Author | Robert A. Elton |
| Volume | 12 |
| Pages | 2179-2189 |
| Publication | Stat Med |
| Date | 1993 |
| Extra | Citation Key: raa93bay tex.citeulike-article-id= 13264720 tex.posted-at= 2014-07-14 14:09:40 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Donald B. Rubin |
| Volume | 9 |
| Pages | 130-134 |
| Publication | Appl Stat |
| Date | 1981 |
| Extra | Citation Key: rub81bay tex.citeulike-article-id= 13264775 tex.posted-at= 2014-07-14 14:09:42 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
points to Efron showing bootstrap distribution of sample proportions
| Type | Journal Article |
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| Author | D. Rubin |
| Author | N. Schenker |
| Volume | 10 |
| Pages | 585-598 |
| Publication | Stat Med |
| Date | 1991 |
| Extra | Citation Key: rub91mul tex.citeulike-article-id= 13264777 tex.posted-at= 2014-07-14 14:09:42 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | G. L. Rosner |
| Author | D. A. Berry |
| URL | http://dx.doi.org/10.1002/sim.4780140405 |
| Volume | 14 |
| Pages | 381-394 |
| Publication | Stat Med |
| Date | 1995 |
| 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 |
| DOI | 10.1002/sim.4780140405 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | Chris T. Volinsky |
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| Author | Adrian E. Raftery |
| Author | Richard A. Kronmal |
| Volume | 46 |
| Pages | 433-448 |
| Publication | Appl Stat |
| Date | 1997 |
| Extra | Citation Key: vol97bay tex.citeulike-article-id= 13265006 tex.posted-at= 2014-07-14 14:09:47 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | Yongxiao Wang |
| Author | Jeremy M. G. Taylor |
| URL | http://dx.doi.org/10.1002/sim.4780141106 |
| Volume | 14 |
| Pages | 1205-1218 |
| Publication | Stat Med |
| Date | 1995 |
| 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 |
| DOI | 10.1002/sim.4780141106 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Robert E. Weiss |
| Volume | 90 |
| Pages | 619-625 |
| Publication | J Am Stat Assoc |
| Date | 1995 |
| Extra | Citation Key: wei95inf tex.citeulike-article-id= 13265035 tex.posted-at= 2014-07-14 14:09:48 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | D. R. Zucker |
| Author | C. H. Schmid |
| Author | M. W. McIntosh |
| Author | R. B. D'Agostino |
| Author | H. P. Selker |
| Author | J. Lau |
| Volume | 50 |
| Pages | 401-410 |
| Publication | J Clin Epi |
| Date | 1997 |
| Extra | Citation Key: zuc97com tex.citeulike-article-id= 13265083 tex.posted-at= 2014-07-14 14:09:48 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Wei Pan |
| Volume | 19 |
| Pages | 1-11 |
| Publication | Stat Med |
| Date | 2000 |
| Extra | Citation Key: pan00two tex.citeulike-article-id= 13265092 tex.posted-at= 2014-07-14 14:09:49 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | David Malakoff |
| URL | http://dx.doi.org/10.1126/science.286.5444.1460 |
| Volume | 286 |
| Pages | 1460-1464 |
| Publication | Science |
| Date | 1999 |
| 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 |
| DOI | 10.1126/science.286.5444.1460 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Chris Hans |
| Volume | 96 |
| Issue | 4 |
| Pages | 835-845 |
| Publication | Biometrika |
| Date | 2009 |
| Extra | Citation Key: han09bay tex.citeulike-article-id= 13265788 tex.posted-at= 2014-07-14 14:10:04 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
advantage of using predicted mean instead of mode
| Type | Journal Article |
|---|---|
| Author | Erol A. Peköz |
| Author | Michael Shwartz |
| Author | Cindy L. Christiansen |
| Author | Dan Berlowitz |
| Volume | 29 |
| Pages | 2180-2193 |
| Publication | Stat Med |
| Date | 2010 |
| Extra | Citation Key: pek10app tex.citeulike-article-id= 13265845 tex.posted-at= 2014-07-14 14:10:05 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Radu V. Xu |
| Author | Lei Sun |
| URL | http://dx.doi.org/10.1214/10-AOAS373 |
| Volume | 5 |
| Issue | 1 |
| Pages | 201-231 |
| Publication | Ann Appl Stat |
| Date | 2011 |
| 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 |
| DOI | 10.1214/10-AOAS373 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Book |
|---|---|
| Author | John K. Kruschke |
| URL | http://www.sciencedirect.com/science/book/9780124058880 |
| Edition | Second Edition |
| Place | Waltham MA |
| Publisher | Academic Press |
| ISBN | 978-0-12-405888-0 |
| Date | 2015 |
| 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 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | John Whitehead |
| Author | Faye Cleary |
| Author | Amanda Turner |
| URL | http://dx.doi.org/10.1002/sim.6469 |
| Volume | 34 |
| Issue | 12 |
| Pages | 2048-2061 |
| Publication | Stat Med |
| ISSN | 02776715 |
| Date | 2015-05 |
| 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 |
| DOI | 10.1002/sim.6469 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Hansheng Wang |
| Author | Shein-Chung Chow |
| Author | Murphy Chen |
| URL | http://dx.doi.org/10.1081/bip-200067789 |
| Volume | 15 |
| Issue | 5 |
| Pages | 799-807 |
| Publication | J Biopharm Stat |
| ISSN | 1054-3406 |
| Date | 2005-09 |
| 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 |
| DOI | 10.1081/bip-200067789 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
analytic form for posterior for normal t-test case
| Type | Journal Article |
|---|---|
| Author | Marvin Zelen |
| Author | Sandra J. Lee |
| URL | http://dx.doi.org/10.1214/ss/1009212752 |
| Volume | 15 |
| Issue | 2 |
| Pages | 95-110 |
| Publication | Stat Sci |
| ISSN | 0883-4237 |
| Date | 2000-05 |
| 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 |
| DOI | 10.1214/ss/1009212752 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
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
| 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 |
| URL | http://dx.doi.org/10.1002/sim.6944 |
| Volume | 35 |
| Issue | 17 |
| Pages | 2955-2974 |
| Publication | Stat Med |
| ISSN | 02776715 |
| Date | 2016-07 |
| 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 |
| DOI | 10.1002/sim.6944 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Book |
|---|---|
| Author | Richard McElreath |
| URL | http://www.worldcat.org/isbn/9781482253443 |
| ISBN | 978-1-4822-5344-3 |
| Date | 2016 |
| 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 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Ward Edwards |
| Author | Harold Lindman |
| Author | Leonard J. Savage |
| URL | http://psycnet.apa.org/doi/10.1037/h0044139 |
| Volume | 70 |
| Issue | 3 |
| Pages | 193-242 |
| Publication | Psych Rev |
| Date | 1963-05 |
| 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 |
| 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 Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Heinz Schmidli |
| Author | Sandro Gsteiger |
| Author | Satrajit Roychoudhury |
| Author | Anthony O'Hagan |
| Author | David Spiegelhalter |
| Author | Beat Neuenschwander |
| URL | http://dx.doi.org/10.1111/biom.12242 |
| Volume | 70 |
| Issue | 4 |
| Pages | 1023-1032 |
| Publication | Biometrics |
| ISSN | 0006341X |
| Date | 2014-12 |
| 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 |
| DOI | 10.1111/biom.12242 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| 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 |
| URL | http://dx.doi.org/10.1002/pst.1807 |
| Publication | Pharm Stat |
| ISSN | 15391604 |
| Date | 2017 |
| 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 |
| DOI | 10.1002/pst.1807 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | A. R. Linero |
| URL | http://dx.doi.org/10.1093/biomet/asx015 |
| Volume | 104 |
| Issue | 2 |
| Pages | 327-341 |
| Publication | Biometrika |
| ISSN | 0006-3444 |
| Date | 2017-06 |
| 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 |
| DOI | 10.1093/biomet/asx015 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Manuscript |
|---|---|
| Author | Andrew Gelman |
| Author | Christian Hennig |
| URL | http://www.stat.columbia.edu/̃gelman/research/published/objectivityr5.pdf |
| Date | 2017-06 |
| 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 |
| Type | Journal Article |
|---|---|
| Author | Steven N. Goodman |
| URL | http://dx.doi.org/10.7326/0003-4819-130-12-199906150-00008 |
| Volume | 130 |
| Issue | 12 |
| Pages | 995+ |
| Publication | Ann Int Med |
| ISSN | 0003-4819 |
| Date | 1999-06 |
| 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 |
| DOI | 10.7326/0003-4819-130-12-199906150-00008 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
Nice language for what happens when scientists use NHST to justify strong statements in their conclusions and interpretation; p-value fallacy
| 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 |
| URL | http://dx.doi.org/10.3758/s13423-017-1343-3 |
| Pages | 1-23 |
| Date | 2017 |
| 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 |
| DOI | 10.3758/s13423-017-1343-3 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | A. P. Dawid |
| Volume | 49 |
| Pages | 325-326 |
| Publication | The Statistician |
| Date | 2000 |
| Extra | Citation Key: daw00com tex.citeulike-article-id= 14438464 tex.posted-at= 2017-09-26 18:54:59 tex.priority= 2 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| 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 |
| URL | http://dx.doi.org/10.1001/jama.2017.14972 |
| Volume | 318 |
| Issue | 16 |
| Pages | 1550+ |
| Publication | JAMA |
| ISSN | 0098-7484 |
| Date | 2017-10 |
| 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 |
| DOI | 10.1001/jama.2017.14972 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Dennis V. Lindley |
| URL | http://dx.doi.org/10.1111/j.1467-9639.1993.tb00252.x |
| Volume | 15 |
| Issue | 1 |
| Pages | 22-25 |
| Publication | Teaching Statistics |
| Date | 1993-03 |
| 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 |
| DOI | 10.1111/j.1467-9639.1993.tb00252.x |
| 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 Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Maria J. Costa |
| Author | Weili He |
| Author | Yannis Jemiai |
| Author | Yueqin Zhao |
| Author | Carl Di Casoli |
| URL | http://dx.doi.org/10.1177/2168479017698190 |
| Volume | 51 |
| Issue | 5 |
| Pages | 568-574 |
| Publication | Therapeutic Innovation & Regulatory Science |
| ISSN | 2168-4790 |
| Date | 2017-04 |
| 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 |
| DOI | 10.1177/2168479017698190 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | William M. Briggs |
| URL | http://dx.doi.org/10.1080/01621459.2017.1311264 |
| Volume | 112 |
| Issue | 519 |
| Pages | 897-898 |
| Publication | JASA |
| Date | 2017-07 |
| 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 |
| DOI | 10.1080/01621459.2017.1311264 |
| 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 Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Kentaro Takeda |
| Author | Satoshi Morita |
| URL | http://dx.doi.org/10.1002/pst.1850 |
| Pages | n/a |
| Publication | Pharm Stat |
| 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 |
| DOI | 10.1002/pst.1850 |
| 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. |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| 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 |
| URL | http://dx.doi.org/10.1002/pst.1595 |
| Volume | 13 |
| Issue | 1 |
| Pages | 3-12 |
| Publication | Pharm Stat |
| ISSN | 15391604 |
| Date | 2014-01 |
| 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 |
| DOI | 10.1002/pst.1595 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Luke Keele |
| Author | Kevin M. Quinn |
| URL | https://doi.org/10.1214/17-AOAS1048 |
| Volume | 11 |
| Issue | 4 |
| Pages | 1974-1997 |
| Publication | Ann App Stat |
| Date | 2017-12 |
| 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 |
| DOI | 10.1214/17-AOAS1048 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Thomas M. Braun |
| URL | http://dx.doi.org/10.1111/biom.12872 |
| Pages | n/a |
| Publication | Biom |
| 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 |
| DOI | 10.1111/biom.12872 |
| 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. |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Paul-Christian Bürkner |
| URL | http://dx.doi.org/10.18637/jss.v080.i01 |
| Volume | 80 |
| Issue | 1 |
| Pages | 1-28 |
| Publication | Journal of Statistical Software |
| Date | 2017 |
| 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 |
| DOI | 10.18637/jss.v080.i01 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Timothy Shin Heng |
| Author | Robert Milan |
| Author | Shing Wan |
| Author | Xueya Zhou |
| Author | Pak Chung |
| URL | http://view.ncbi.nlm.nih.gov/pubmed/28480976 |
| Volume | 41 |
| Issue | 6 |
| Pages | 469-480 |
| Publication | Gen Epi |
| ISSN | 1098-2272 |
| Date | 2017-09 |
| 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 |
| 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 Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
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.
| Type | Journal Article |
|---|---|
| Author | Maria J. Costa |
| Author | Thomas Drury |
| URL | http://dx.doi.org/10.1002/pst.1852 |
| Publication | Pharm Stat |
| ISSN | 15391604 |
| Date | 2018-02 |
| 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 |
| DOI | 10.1002/pst.1852 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| 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 |
| URL | https://www.jstatsoft.org/v076/i01 |
| Volume | 76 |
| Issue | 1 |
| Pages | 1-32 |
| Publication | J Stat Software |
| Date | 2017 |
| 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 |
| DOI | 10.18637/jss.v076.i01 |
| 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 Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | John K. Kruschke |
| URL | http://dx.doi.org/10.1037/a0029146 |
| Volume | 142 |
| Issue | 2 |
| Pages | 573-603 |
| Publication | J Exp Psych |
| ISSN | 1939-2222 |
| Date | 2013-05 |
| 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 |
| DOI | 10.1037/a0029146 |
| 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 Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Yulei He |
| Author | Frederic Selck |
| Author | Sharon-Lise T. Normand |
| URL | http://dx.doi.org/10.1002/sim.6012 |
| Volume | 33 |
| Issue | 7 |
| Pages | 1081-1103 |
| Publication | Stat Med |
| Date | 2014-03 |
| 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 |
| DOI | 10.1002/sim.6012 |
| 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 Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | David J. Nott |
| Author | Chenlei Leng |
| URL | http://dx.doi.org/10.1016/j.csda.2010.01.036 |
| Volume | 54 |
| Issue | 12 |
| Pages | 3227-3241 |
| Publication | Computational Statistics & Data Analysis |
| ISSN | 01679473 |
| Date | 2010-12 |
| 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 |
| DOI | 10.1016/j.csda.2010.01.036 |
| 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 Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Book |
|---|---|
| Author | Nate Silver |
| URL | http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0143125087 |
| Edition | 1 |
| Publisher | Penguin Books |
| ISBN | 0-14-312508-7 |
| Date | 2012 |
| 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 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Andrew Gelman |
| URL | http://dx.doi.org/10.1002/sim.6427 |
| Volume | 34 |
| Issue | 7 |
| Pages | 1259-1260 |
| Publication | Stat Med |
| ISSN | 02776715 |
| Date | 2015-03 |
| 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 |
| DOI | 10.1002/sim.6427 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Andrew Gelman |
| URL | http://dx.doi.org/10.1097/ede.0b013e31827886f7 |
| Volume | 24 |
| Issue | 1 |
| Pages | 69-72 |
| Publication | Epi |
| ISSN | 1044-3983 |
| Date | 2013-01 |
| 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 |
| DOI | 10.1097/ede.0b013e31827886f7 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Gengqian Cai |
| Author | Tianhui Zhou |
| URL | http://dx.doi.org/10.1002/sim.4445 |
| Volume | 31 |
| Issue | 16 |
| Pages | 1774-1776 |
| Publication | Stat Med |
| Date | 2012 |
| 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 |
| DOI | 10.1002/sim.4445 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
concise equation for normal approximation to Bayesian predictive probability
| Type | Journal Article |
|---|---|
| Author | Jack J. Lee |
| Author | Caleb T. Chu |
| URL | http://dx.doi.org/10.1002/sim.5404 |
| Volume | 31 |
| Issue | 25 |
| Pages | 2955-2972 |
| Publication | Stat Med |
| Date | 2012 |
| 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 |
| DOI | 10.1002/sim.5404 |
| 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 Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | H. P. Lehmann |
| Author | M. R. Wachter |
| URL | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2232964/ |
| Pages | 433-437 |
| Publication | Proceedings : a conference of the American Medical Informatics Association / ... AMIA Annual Fall Symposium. AMIA Fall Symposium |
| ISSN | 1091-8280 |
| Date | 1996 |
| 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 |
| 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 Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | A. Lawrence Gould |
| Author | Xiaohua D. Zhang |
| URL | http://dx.doi.org/10.1002/sim.5993 |
| Volume | 33 |
| Issue | 6 |
| Pages | 940-957 |
| Publication | Stat Med |
| Date | 2014-03 |
| 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 |
| DOI | 10.1002/sim.5993 |
| 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 Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Y. T. Lurdes |
| Author | P. F. Thall |
| Author | D. A. Berry |
| URL | http://dx.doi.org/10.1111/j.0006-341X.2002.00823.x |
| Volume | 58 |
| Issue | 4 |
| Pages | 823-831 |
| Publication | Biometrics |
| Date | 2002 |
| 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 |
| DOI | 10.1111/j.0006-341X.2002.00823.x |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Heinz Schmidli |
| Author | Frank Bretz |
| Author | Amy Racine-Poon |
| Volume | 26 |
| Pages | 4925-4938 |
| Publication | Stat Med |
| Date | 2007 |
| Extra | Citation Key: sch07bay tex.citeulike-article-id= 13265654 tex.posted-at= 2014-07-14 14:10:01 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Juned Siddique |
| Volume | 27 |
| Pages | 83-102 |
| Publication | Stat Med |
| Date | 2008 |
| Extra | Citation Key: sid08mul tex.citeulike-article-id= 13265659 tex.posted-at= 2014-07-14 14:10:01 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Pierpaolo Brutti |
| Author | Fulvio De Santis |
| Author | Stefania Gubbiotti |
| Volume | 27 |
| Pages | 2290-2306 |
| Publication | Stat Med |
| Date | 2008 |
| Extra | Citation Key: bru08rob tex.citeulike-article-id= 13265677 tex.posted-at= 2014-07-14 14:10:01 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Stephen Senn |
| Volume | 62 |
| Issue | 3 |
| Pages | 206-210 |
| Publication | Am Statistician |
| Date | 2008 |
| Extra | Citation Key: sen08not tex.citeulike-article-id= 13265685 tex.posted-at= 2014-07-14 14:10:02 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
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>."
| 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 |
| Volume | 28 |
| Pages | 1445-1463 |
| Publication | Stat Med |
| Date | 2009 |
| Extra | Citation Key: bra09con tex.citeulike-article-id= 13265757 tex.posted-at= 2014-07-14 14:10:03 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
nice display of how to formalize the use of posterior probabilities for deciding the next step in the trial;RCT
| Type | Journal Article |
|---|---|
| Author | David Lunn |
| Author | David Spiegelhalter |
| Author | Andrew Thomas |
| Author | Nicky Best |
| Volume | 28 |
| Pages | 3049-3082 |
| Publication | Stat Med |
| Date | 2009 |
| Extra | Citation Key: lun09bug tex.citeulike-article-id= 13265785 tex.posted-at= 2014-07-14 14:10:04 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Pierpaolo Brutti |
| Author | Fulvio De Santis |
| Author | Stefania Gubbiotti |
| Volume | 28 |
| Pages | 2185-2201 |
| Publication | Stat Med |
| Date | 2009 |
| Extra | Citation Key: bru09mix tex.citeulike-article-id= 13265772 tex.posted-at= 2014-07-14 14:10:03 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Lawrence Joseph |
| Author | Patrick Belisle |
| Author | Hala Tamim |
| Author | John S. Sampalis |
| Volume | 57 |
| Pages | 147-153 |
| Publication | J Clin Epi |
| Date | 2004 |
| Extra | Citation Key: jos04sel tex.citeulike-article-id= 13265364 tex.posted-at= 2014-07-14 14:09:55 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Paul C. Lambert |
| Author | Alex J. Sutton |
| Author | Paul R. Burton |
| Author | Keith R. Abrams |
| Author | David R. Jones |
| Volume | 24 |
| Pages | 2401-2428 |
| Publication | Stat Med |
| Date | 2005 |
| Extra | Citation Key: lam05how tex.citeulike-article-id= 13265432 tex.posted-at= 2014-07-14 14:09:56 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
uninformative prior distributions showed large effects of results for scale parameters in meta-analysis with small numbers of studies;beautiful graphical summaries of results
| Type | Journal Article |
|---|---|
| Author | C. Conigliani |
| Author | A. Tancredi |
| Volume | 24 |
| Pages | 3171-3184 |
| Publication | Stat Med |
| Date | 2005 |
| Extra | Citation Key: con05sem tex.citeulike-article-id= 13265451 tex.posted-at= 2014-07-14 14:09:57 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Deborah Ashby |
| Volume | 25 |
| Pages | 3589-3631 |
| Publication | Stat Med |
| Date | 2006 |
| Extra | Citation Key: ash06bay tex.citeulike-article-id= 13265537 tex.posted-at= 2014-07-14 14:09:58 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Paul Gustafson |
| Author | Sander Greenland |
| Volume | 62 |
| Pages | 760-768 |
| Publication | Biometrics |
| Date | 2006 |
| Extra | Citation Key: gus06per tex.citeulike-article-id= 13265538 tex.posted-at= 2014-07-14 14:09:58 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Stephen Senn |
| Volume | 26 |
| Pages | 1417-1430 |
| Publication | Stat Med |
| Date | 2007 |
| Extra | Citation Key: sen07try tex.citeulike-article-id= 13265565 tex.posted-at= 2014-07-14 14:09:59 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Ohlssen |
| Author | L. D. Sharples |
| Author | D. J. Spiegelhalter |
| Volume | 26 |
| Pages | 2088-2112 |
| Publication | Stat Med |
| Date | 2007 |
| Extra | Citation Key: ohl07fle tex.citeulike-article-id= 13265568 tex.posted-at= 2014-07-14 14:09:59 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Amy H. Herring |
| Author | Juan Yang |
| Volume | 63 |
| Pages | 381-388 |
| Publication | Biometrics |
| Date | 2007 |
| Extra | Citation Key: her07bay tex.citeulike-article-id= 13265599 tex.posted-at= 2014-07-14 14:10:00 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Juanjuan Fan |
| Author | Richard A. Levine |
| Volume | 20 |
| Issue | 3 |
| Pages | 26-32 |
| Publication | Chance |
| Date | 2007 |
| Extra | Citation Key: fan07amn tex.citeulike-article-id= 13265622 tex.posted-at= 2014-07-14 14:10:00 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Jean-Marie Grouin |
| Author | Maylis Coste |
| Author | Pierre Bunouf |
| Author | Bruno Lecoutre |
| Volume | 26 |
| Pages | 4914-4924 |
| Publication | Stat Med |
| Date | 2007 |
| Extra | Citation Key: gro07bay tex.citeulike-article-id= 13265637 tex.posted-at= 2014-07-14 14:10:00 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Scott S. Emerson |
| Author | John M. Kittelson |
| Author | Daniel L. Gillen |
| URL | http://dx.doi.org/10.1002/sim.2901 |
| Volume | 26 |
| Pages | 5047-5080 |
| Publication | Stat Med |
| Date | 2007 |
| 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 |
| DOI | 10.1002/sim.2901 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Various |
| Volume | 20 |
| Issue | 3 |
| Publication | Stat Med |
| Date | 2001 |
| Extra | Citation Key: cluster.rct tex.citeulike-article-id= 13265182 tex.posted-at= 2014-07-14 14:09:51 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Donald J. Malec |
| Volume | 20 |
| Pages | 1363-1371 |
| Publication | Stat Med |
| Date | 2001 |
| Extra | Citation Key: mal01con tex.citeulike-article-id= 13265192 tex.posted-at= 2014-07-14 14:09:51 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | A. Vail |
| Author | J. Hornbuckle |
| Author | D. J. Spiegelhalter |
| Author | J. G. Thornton |
| URL | http://dx.doi.org/10.1002/sim.1171 |
| Volume | 20 |
| Pages | 3777-3787 |
| Publication | Stat Med |
| Date | 2001 |
| 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 |
| DOI | 10.1002/sim.1171 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
use of stylized priors because of substantial variability of prior opinions;interim results released to investigators
| Type | Journal Article |
|---|---|
| Author | Donald A. Berry |
| Volume | 9 |
| Issue | 2 |
| Pages | 1-11 |
| Publication | Biopharm Rep ASA |
| Date | 2001 |
| Extra | Citation Key: ber01ada tex.citeulike-article-id= 13265268 tex.posted-at= 2014-07-14 14:09:53 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
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)
| Type | Journal Article |
|---|---|
| Author | Lawrence Joseph |
| Author | David B. Wolfson |
| Volume | 46 |
| Pages | 145-149 |
| Publication | The Statistician |
| Date | 1997 |
| Extra | Citation Key: jos97int tex.citeulike-article-id= 13265275 tex.posted-at= 2014-07-14 14:09:53 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Lawrence Joseph |
| Author | Patrick Bélisle |
| Volume | 46 |
| Pages | 209-226 |
| Publication | The Statistician |
| Date | 1997 |
| Extra | Citation Key: jos97baya tex.citeulike-article-id= 13265276 tex.posted-at= 2014-07-14 14:09:53 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | C. J. Adcock |
| Volume | 46 |
| Pages | 261-283 |
| Publication | The Statistician |
| Date | 1997 |
| Extra | Citation Key: adc97sam tex.citeulike-article-id= 13265277 tex.posted-at= 2014-07-14 14:09:53 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Hamid Pezeshk |
| Author | John Gittins |
| Volume | 36 |
| Pages | 143-150 |
| Publication | Drug Info J |
| Date | 2002 |
| Extra | Citation Key: pez02ful tex.citeulike-article-id= 13265273 tex.posted-at= 2014-07-14 14:09:53 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Willi Sauerbrei |
| Author | Martin Schumacher |
| Volume | 11 |
| Pages | 2093-2109 |
| Publication | Stat Med |
| Date | 1992 |
| Extra | Citation Key: sau92boo tex.citeulike-article-id= 13264795 tex.posted-at= 2014-07-14 14:09:42 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Gideon Schwarz |
| Volume | 6 |
| Pages | 461-464 |
| Publication | Ann Stat |
| Date | 1978 |
| Extra | Citation Key: sch78est tex.citeulike-article-id= 13264797 tex.posted-at= 2014-07-14 14:09:42 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Pamela E. Scott |
| Author | Gregory Campbell |
| Volume | 32 |
| Pages | 213-220 |
| Publication | Drug Info J |
| Date | 1998 |
| Extra | Citation Key: sco98int tex.citeulike-article-id= 13264822 tex.posted-at= 2014-07-14 14:09:42 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Lewis B. Sheiner |
| URL | http://dx.doi.org/10.1038/clpt.1991.97 |
| Volume | 50 |
| Pages | 4-9 |
| Publication | Clin Pharm Ther |
| Date | 1991 |
| 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 |
| DOI | 10.1038/clpt.1991.97 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
problems with traditional statistical approaches to drug evaluation;problems with under-emphasis of type II error
| Type | Journal Article |
|---|---|
| Author | Debajyoti Sinha |
| Author | Dipak K. Dey |
| Volume | 92 |
| Pages | 1195-1212 |
| Publication | J Am Stat Assoc |
| Date | 1997 |
| Extra | Citation Key: sin97sem tex.citeulike-article-id= 13264866 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | A. F. M. Smith |
| Author | A. E. Gelfand |
| Volume | 46 |
| Pages | 84-88 |
| Publication | Am Statistician |
| Date | 1992 |
| Extra | Citation Key: smi92bay tex.citeulike-article-id= 13264874 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Teresa A. Smith |
| Author | David J. Spiegelhalter |
| Author | Andrew Thomas |
| Volume | 14 |
| Pages | 2685-2699 |
| Publication | Stat Med |
| Date | 1995 |
| Extra | Citation Key: smi95bay tex.citeulike-article-id= 13264878 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
advantages over DerSimonian and Laird which ignores uncertainty in some parameter estimates
| Type | Journal Article |
|---|---|
| Author | A. F. M. Smith |
| Author | D. J. Spiegelhalter |
| Volume | 42 |
| Pages | 213-220 |
| Publication | J Roy Stat Soc B |
| Date | 1980 |
| Extra | Citation Key: smi80bay tex.citeulike-article-id= 13264872 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | David J. Spiegelhalter |
| Author | L. S. Freedman |
| Author | M. K. B. Parmar |
| URL | http://dx.doi.org/10.1002/sim.4780121516 |
| Volume | 12 |
| Pages | 1501-1511 |
| Publication | Stat Med |
| Date | 1993 |
| 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 |
| DOI | 10.1002/sim.4780121516 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | David J. Spiegelhalter |
| Author | Lawrence S. Freedman |
| URL | http://dx.doi.org/10.1002/sim.4780050103 |
| Volume | 5 |
| Pages | 1-13 |
| Publication | Stat Med |
| Date | 1986 |
| 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 |
| DOI | 10.1002/sim.4780050103 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Dalene K. Stangl |
| Volume | 14 |
| Pages | 2173-2190 |
| Publication | Stat Med |
| Date | 1995 |
| Extra | Citation Key: sta95pre tex.citeulike-article-id= 13264901 tex.posted-at= 2014-07-14 14:09:45 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Nigel Stallard |
| Volume | 54 |
| Pages | 279-294 |
| Publication | Biometrics |
| Date | 1998 |
| Extra | Citation Key: sta98sam tex.citeulike-article-id= 13264902 tex.posted-at= 2014-07-14 14:09:45 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Ming Tan |
| Author | Yinsheng Qu |
| Author | Ed Mascha |
| Author | Armin Schubert |
| Volume | 18 |
| Pages | 1983-1992 |
| Publication | Stat Med |
| Date | 1999 |
| Extra | Citation Key: tan99bay tex.citeulike-article-id= 13264930 tex.posted-at= 2014-07-14 14:09:46 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Peter F. Thall |
| Author | Hsi-Guang Sung |
| Volume | 17 |
| Pages | 1563-1580 |
| Publication | Stat Med |
| Date | 1998 |
| Extra | Citation Key: tha98som tex.citeulike-article-id= 13264945 tex.posted-at= 2014-07-14 14:09:46 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Neal Thomas |
| Author | Nicholas Longford |
| Author | John E. Rolph |
| Volume | 13 |
| Pages | 889-903 |
| Publication | Stat Med |
| Date | 1994 |
| Extra | Citation Key: tho94emp tex.citeulike-article-id= 13264953 tex.posted-at= 2014-07-14 14:09:46 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Simon G. Thompson |
| Author | Teresa C. Smith |
| Author | Stephen J. Sharp |
| Volume | 16 |
| Pages | 2741-2758 |
| Publication | Stat Med |
| 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 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Robert Tibshirani |
| Volume | 76 |
| Pages | 604-608 |
| Publication | Biometrika |
| Date | 1989 |
| Extra | Citation Key: tib89non tex.citeulike-article-id= 13264960 tex.posted-at= 2014-07-14 14:09:46 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | N. Turkkan |
| Author | T. Pham-Gia |
| Volume | 46 |
| Pages | 131-140 |
| Publication | Appl Stat |
| Date | 1997 |
| Extra | Citation Key: tur97hig tex.citeulike-article-id= 13264983 tex.posted-at= 2014-07-14 14:09:47 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Yutaka Matsuyama |
| Author | Junichi Sakamoto |
| Author | Yasuo Ohashi |
| URL | http://dx.doi.org/10.1002/(SICI)1097-0258(19980915)17:17%3C1893::AID-SIM878%3E3.3.CO;2-I |
| Volume | 17 |
| Pages | 1893-1908 |
| Publication | Stat Med |
| Date | 1998 |
| 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 |
| 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 |
| Type | Journal Article |
|---|---|
| Author | T. J. Mitchell |
| Author | J. J. Beauchamp |
| Volume | 83 |
| Pages | 1023-1036 |
| Publication | J Am Stat Assoc |
| Date | 1988 |
| Extra | Citation Key: mit88bay tex.citeulike-article-id= 13264600 tex.posted-at= 2014-07-14 14:09:38 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Pietro Muliere |
| Author | Stephen Walker |
| Volume | 60 |
| Pages | 175-182 |
| Publication | J Roy Stat Soc B |
| Date | 1998 |
| Extra | Citation Key: mul98ext tex.citeulike-article-id= 13264611 tex.posted-at= 2014-07-14 14:09:38 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Michael A. Newton |
| Author | Adrian E. Rafter |
| Volume | 56 |
| Pages | 3-48 |
| Publication | J Roy Stat Soc B |
| Date | 1994 |
| Extra | Citation Key: new94app tex.citeulike-article-id= 13264622 tex.posted-at= 2014-07-14 14:09:39 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Keith O'Rourke |
| Volume | 17 |
| Pages | 350-351 |
| Publication | Controlled Clin Trials |
| Date | 1996 |
| Extra | Citation Key: oro96two tex.citeulike-article-id= 13264647 tex.posted-at= 2014-07-14 14:09:39 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | Robert L. Obenchain |
| Author | Bryan M. Johnstone |
| Volume | 33 |
| Pages | 191-209 |
| Publication | Drug Info J |
| Date | 1999 |
| Extra | Citation Key: obe99mix tex.citeulike-article-id= 13264636 tex.posted-at= 2014-07-14 14:09:39 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | Purushottam W. Laud |
| Author | Joseph G. Ibrahim |
| Volume | 57 |
| Pages | 247-262 |
| Publication | J Roy Stat Soc B |
| Date | 1995 |
| Extra | Citation Key: lau95pre tex.citeulike-article-id= 13264465 tex.posted-at= 2014-07-14 14:09:36 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Harold P. Lehmann |
| Author | Bach Nguyen |
| URL | http://www.ncbi.nlm.nih.gov/pubmed/9308343 |
| Volume | 14 |
| Issue | 5 |
| Pages | 353-359 |
| Publication | M.D. Computing |
| Date | 1997 |
| 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 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Wendy Leisenring |
| Author | Margaret S. Pepe |
| Volume | 54 |
| Pages | 444-452 |
| Publication | Biometrics |
| Date | 1998 |
| Extra | Citation Key: lei98reg tex.citeulike-article-id= 13264499 tex.posted-at= 2014-07-14 14:09:36 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | R. J. Lilford |
| Author | D. Braunholtz |
| Volume | 313 |
| Pages | 603-607 |
| Publication | BMJ |
| Date | 1996 |
| Extra | Citation Key: lil96sta tex.citeulike-article-id= 13264515 tex.posted-at= 2014-07-14 14:09:37 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Stuart R. Lipsitz |
| Author | Lue P. Zhao |
| Author | Geert Molenberghs |
| Volume | 60 |
| Pages | 127-144 |
| Publication | J Roy Stat Soc B |
| Date | 1998 |
| Extra | Citation Key: lip98sem tex.citeulike-article-id= 13264534 tex.posted-at= 2014-07-14 14:09:37 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Lobat Hashemi |
| Author | Balgobin Nandram |
| Author | Robert Goldberg |
| Volume | 16 |
| Pages | 1311-1328 |
| Publication | Stat Med |
| Date | 1997 |
| Extra | Citation Key: has97bay tex.citeulike-article-id= 13264272 tex.posted-at= 2014-07-14 14:09:32 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Daniel F. Heitjan |
| Volume | 16 |
| Pages | 1791-1802 |
| Publication | Stat Med |
| Date | 1997 |
| Extra | Citation Key: hei97bay tex.citeulike-article-id= 13264283 tex.posted-at= 2014-07-14 14:09:32 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | John C. Hornberger |
| Author | Byron W. Brown |
| Author | Jerry Halpern |
| URL | http://dx.doi.org/10.1002/sim.4780142008 |
| Volume | 14 |
| Pages | 2249-2259 |
| Publication | Stat Med |
| Date | 1995 |
| 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 |
| DOI | 10.1002/sim.4780142008 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | John S. J. Hsu |
| Author | Tom Leonard |
| Volume | 84 |
| Pages | 85-93 |
| Publication | Biometrika |
| Date | 1997 |
| Extra | Citation Key: hsu97hie tex.citeulike-article-id= 13264339 tex.posted-at= 2014-07-14 14:09:33 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | Hans C. van Houwelingen |
| Author | Jane Thorogood |
| Volume | 14 |
| Pages | 1999-2008 |
| Publication | Stat Med |
| Date | 1995 |
| Extra | Citation Key: hou95con tex.citeulike-article-id= 13264331 tex.posted-at= 2014-07-14 14:09:33 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Book |
|---|---|
| Author | C. Howson |
| Author | P. Urbach |
| Place | La Salle, IL |
| Publisher | Open Court |
| Date | 1989 |
| Extra | Citation Key: how89sci tex.citeulike-article-id= 13264332 tex.posted-at= 2014-07-14 14:09:33 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
Written by two philosophers, and has no pretense of being objective.
| Type | Journal Article |
|---|---|
| Author | J. V. Howard |
| Volume | 13 |
| Pages | 351-367 |
| Publication | Stat Sci |
| Date | 1998 |
| Extra | Citation Key: how982x2 tex.citeulike-article-id= 13264333 tex.posted-at= 2014-07-14 14:09:33 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | Michael D. Hughes |
| Volume | 12 |
| Pages | 1651-1663 |
| Publication | Stat Med |
| Date | 1993 |
| Extra | Citation Key: hug93rep tex.citeulike-article-id= 13264343 tex.posted-at= 2014-07-14 14:09:33 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | Jennison |
| Volume | 5 |
| Pages | 299-317 |
| Publication | Stat Sci |
| Date | 1990 |
| Extra | Citation Key: jen90sta tex.citeulike-article-id= 13264360 tex.posted-at= 2014-07-14 14:09:33 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
dealing with final estimates and confidence limits
| Type | Journal Article |
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| Author | Lawrence Joseph |
| Author | Roxane Du Berger |
| Author | Patrick Bélisle |
| Volume | 16 |
| Pages | 769-781 |
| Publication | Stat Med |
| Date | 1997 |
| Extra | Citation Key: jos97bayb tex.citeulike-article-id= 13264371 tex.posted-at= 2014-07-14 14:09:34 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | Robert E. Kass |
| Author | Larry Wasserman |
| Volume | 90 |
| Pages | 928-934 |
| Publication | J Am Stat Assoc |
| Date | 1995 |
| Extra | Citation Key: kas95ref tex.citeulike-article-id= 13264388 tex.posted-at= 2014-07-14 14:09:34 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | Robert E. Kass |
| Author | Larry Wasserman |
| Volume | 91 |
| Pages | 1343-1370 |
| Publication | J Am Stat Assoc |
| Date | 1996 |
| Extra | Citation Key: kas96sel tex.citeulike-article-id= 13264389 tex.posted-at= 2014-07-14 14:09:34 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | Daniel T. Larose |
| Author | Dipak K. Dey |
| Volume | 16 |
| Pages | 1817-1829 |
| Publication | Stat Med |
| Date | 1997 |
| Extra | Citation Key: lar97gro tex.citeulike-article-id= 13264463 tex.posted-at= 2014-07-14 14:09:36 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | R. D. Etzioni |
| Author | J. B. Kadane |
| Volume | 16 |
| Pages | 23-41 |
| Publication | Ann Rev Pub Hlth |
| Date | 1995 |
| Extra | Citation Key: etz95bay tex.citeulike-article-id= 13264055 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Bradley Efron |
| Volume | 80 |
| Pages | 3-26 |
| Publication | Biometrika |
| Date | 1993 |
| Extra | Citation Key: efr93bay tex.citeulike-article-id= 13264041 tex.posted-at= 2014-07-14 14:09:27 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Peter M. Fayers |
| Author | Deborah Ashby |
| Author | Mahesh K. Parmar |
| Volume | 16 |
| Pages | 1413-1430 |
| Publication | Stat Med |
| Date | 1997 |
| Extra | Citation Key: fay97bay tex.citeulike-article-id= 13264065 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Lloyd D. Fisher |
| Volume | 17 |
| Pages | 423-434 |
| Publication | Controlled Clin Trials |
| Date | 1996 |
| Extra | Citation Key: fis96com tex.citeulike-article-id= 13264077 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | David Faraggi |
| Author | Richard Simon |
| Volume | 16 |
| Pages | 2573-2585 |
| Publication | Stat Med |
| Date | 1997 |
| Extra | Citation Key: far97lar tex.citeulike-article-id= 13264064 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | Laurence Freedman |
| Volume | 313 |
| Pages | 569-570 |
| Publication | BMJ |
| Date | 1996 |
| Extra | Citation Key: fre96bay tex.citeulike-article-id= 13264103 tex.posted-at= 2014-07-14 14:09:29 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | S. L. George |
| Author | C. Li |
| Author | D. A. Berry |
| Author | M. R. Green |
| URL | http://dx.doi.org/10.1002/sim.4780131305 |
| Volume | 13 |
| Pages | 1313-1328 |
| Publication | Stat Med |
| Date | 1994 |
| 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 |
| DOI | 10.1002/sim.4780131305 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
nice graph showing updating of posterior
| Type | Journal Article |
|---|---|
| Author | Geof H. Givens |
| Author | D. D. Smith |
| Author | R. L. Tweedie |
| Volume | 12 |
| Pages | 221-250 |
| Publication | Stat Sci |
| Date | 1997 |
| Extra | Citation Key: giv97pub tex.citeulike-article-id= 13264137 tex.posted-at= 2014-07-14 14:09:29 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Harvey Goldstein |
| Author | David J. Spiegelhalter |
| Volume | 159 |
| Pages | 385-443 |
| Publication | J Roy Stat Soc A |
| Date | 1996 |
| Extra | Citation Key: gol96lea tex.citeulike-article-id= 13264154 tex.posted-at= 2014-07-14 14:09:30 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | David G. T. Denison |
| Author | Bani K. Mallick |
| Author | Adrian F. M. Smith |
| Volume | 85 |
| Pages | 363-377 |
| Publication | Biometrika |
| Date | 1998 |
| Extra | Citation Key: den98bay tex.citeulike-article-id= 13263990 tex.posted-at= 2014-07-14 14:09:27 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | D. O. Dixon |
| Author | R. Simon |
| Volume | 47 |
| Pages | 871-881 |
| Publication | Biometrics |
| Date | 1991 |
| Extra | Citation Key: dix91bay tex.citeulike-article-id= 13264004 tex.posted-at= 2014-07-14 14:09:27 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | R. DerSimonian |
| Author | N. Laird |
| Volume | 7 |
| Pages | 177-188 |
| Publication | Controlled Clin Trials |
| Date | 1986 |
| Extra | Citation Key: der86met tex.citeulike-article-id= 13263991 tex.posted-at= 2014-07-14 14:09:27 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
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| Author | D. O. Dixon |
| Author | R. Simon |
| Volume | 11 |
| Pages | 13-22 |
| Publication | Stat Med |
| Date | 1992 |
| Extra | Citation Key: dix92bay tex.citeulike-article-id= 13264005 tex.posted-at= 2014-07-14 14:09:27 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Bradley Efron |
| Author | Carl Morris |
| Volume | 70 |
| Pages | 311-319 |
| Publication | J Am Stat Assoc |
| Date | 1975 |
| Extra | Citation Key: efr75dat tex.citeulike-article-id= 13264031 tex.posted-at= 2014-07-14 14:09:27 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Mahmoud A. El-Gamal |
| Author | David M. Grether |
| Volume | 90 |
| Pages | 1137-1145 |
| Publication | J Am Stat Assoc |
| Date | 1995 |
| Extra | Citation Key: elg95peo tex.citeulike-article-id= 13264045 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Scott S. Emerson |
| Volume | 51 |
| Pages | 1152-1162 |
| Publication | Biometrics |
| Date | 1995 |
| Extra | Citation Key: eme95sto tex.citeulike-article-id= 13264046 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Steven N. Goodman |
| Volume | 11 |
| Pages | 875-879 |
| Publication | Stat Med |
| Date | 1998 |
| Extra | Citation Key: goo92com tex.citeulike-article-id= 13264159 tex.posted-at= 2014-07-14 14:09:30 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Joel B. Greenhouse |
| Author | Larry Wasserman |
| Volume | 14 |
| Pages | 1379-1391 |
| Publication | Stat Med |
| Date | 1995 |
| Extra | Citation Key: gre95rob tex.citeulike-article-id= 13264182 tex.posted-at= 2014-07-14 14:09:30 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | A. P. Grieve |
| Volume | 17 |
| Pages | 1715-1723 |
| Publication | Stat Med |
| Date | 1998 |
| Extra | Citation Key: gri98iss tex.citeulike-article-id= 13264187 tex.posted-at= 2014-07-14 14:09:30 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Jason Grossman |
| Author | Mahesh K. B. Parmar |
| Author | David J. Spiegelhalter |
| Volume | 13 |
| Pages | 1815-1826 |
| Publication | Stat Med |
| Date | 1994 |
| Extra | Citation Key: gro94uni tex.citeulike-article-id= 13264189 tex.posted-at= 2014-07-14 14:09:30 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Paul R. Burton |
| Volume | 1994 |
| Pages | 1699-1713 |
| Publication | Stat Med |
| Date | 1994 |
| Extra | Citation Key: bur94hel tex.citeulike-article-id= 13263835 tex.posted-at= 2014-07-14 14:09:24 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Shelley B. Bull |
| Volume | 17 |
| Pages | 2179-2197 |
| Publication | Stat Med |
| Date | 1998 |
| Extra | Citation Key: bul98reg tex.citeulike-article-id= 13263831 tex.posted-at= 2014-07-14 14:09:24 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | D. J. Spiegelhalter |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.4780050506 |
| Volume | 5 |
| Pages | 421-433 |
| Publication | Stat Med |
| Date | 1986 |
| Extra | Citation Key: spi86 |
| DOI | 10.1002/sim.4780050506 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
z-test for calibration inaccuracy (implemented in Stata, and R Hmisc package's val.prob function)
| Type | Journal Article |
|---|---|
| Author | David J. Spiegelhalter |
| Author | Laurence S. Freedman |
| Author | Mahesh K. B. Parmar |
| URL | https://doi.org/10.2307/2983527 |
| Volume | 157 |
| Pages | 357-416 |
| Publication | J Roy Stat Soc A |
| Date | 1994 |
| Extra | Citation Key: spi94bay tex.citeulike-article-id= 13264891 tex.posted-at= 2014-07-14 14:09:45 tex.priority= 0 |
| DOI | 10.2307/2983527 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Nigel Dallow |
| Author | Nicky Best |
| Author | Timothy H. Montague |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1854 |
| Volume | 0 |
| Issue | 0 |
| Publication | Pharm Stat |
| Date | 2018 |
| 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 |
| DOI | 10.1002/pst.1854 |
| 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 Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Book Section |
|---|---|
| Author | Donald A. Berry |
| Editor | J. M. Bernardo |
| Editor | M. H. DeGroot |
| Editor | D. V. Lindley |
| Editor | A. F. M. Smith |
| Volume | 3 |
| Publisher | Oxford University Press |
| Pages | 79-94 |
| Date | 1988 |
| Extra | Citation Key: ber88mul tex.citeulike-article-id= 13263748 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0 |
| Book Title | Bayesian Statistics |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
if want to dredge data to generate hypotheses one still needs another dataset to test the hypotheses
| Type | Journal Article |
|---|---|
| Author | Sander Greenland |
| URL | http://dx.doi.org/10.1111/j.0006-341X.2000.00915.x |
| Volume | 56 |
| Pages | 915-921 |
| Publication | Biometrics |
| Date | 2000 |
| 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 |
| 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 |
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.
| Type | Journal Article |
|---|---|
| Author | Keith Abrams |
| Author | Deborah Ashby |
| Author | Doug Errington |
| Volume | 15 |
| Pages | 349-359 |
| Publication | Controlled Clin Trials |
| Date | 1994 |
| Extra | Citation Key: abr94sim tex.citeulike-article-id= 13263671 tex.posted-at= 2014-07-14 14:09:20 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | James H. Albert |
| Volume | 47 |
| Pages | 182-191 |
| Publication | Am Statistician |
| Date | 1993 |
| Extra | Citation Key: alb93tea tex.citeulike-article-id= 13263681 tex.posted-at= 2014-07-14 14:09:21 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | Elja Arjas |
| Author | Liping Liu |
| Volume | 15 |
| Pages | 1757-1770 |
| Publication | Stat Med |
| Date | 1996 |
| Extra | Citation Key: arj96non tex.citeulike-article-id= 13263706 tex.posted-at= 2014-07-14 14:09:21 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Type | Journal Article |
|---|---|
| Author | John K. Kruschke |
| Author | Torrin M. Liddell |
| URL | http://dx.doi.org/10.3758/s13423-017-1272-1 |
| Pages | 1-23 |
| Date | 2017 |
| 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 |
| DOI | 10.3758/s13423-017-1272-1 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
Excellent for teaching Bayesian methods and explaining the advantages
| Type | Journal Article |
|---|---|
| Author | Laurence S. Freedman |
| Author | David J. Spiegelhalter |
| Author | Mahesh K. B. Parmar |
| Volume | 13 |
| Pages | 1371-1383 |
| Publication | Stat Med |
| Date | 1994 |
| Extra | Citation Key: fre94wha tex.citeulike-article-id= 13264101 tex.posted-at= 2014-07-14 14:09:29 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
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
| Type | Journal Article |
|---|---|
| Author | Robin Henderson |
| Volume | 14 |
| Pages | 161-184 |
| Publication | Stat Med |
| Date | 1995 |
| Extra | Citation Key: hen95pro tex.citeulike-article-id= 13264286 tex.posted-at= 2014-07-14 14:09:32 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
predicting survival times and probabilities from Cox model
| Type | Journal Article |
|---|---|
| Author | Joseph B. Kadane |
| Volume | 17 |
| Pages | 352 |
| Publication | Controlled Clin Trials |
| Date | 1996 |
| Extra | Citation Key: kad96rep tex.citeulike-article-id= 13264374 tex.posted-at= 2014-07-14 14:09:34 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
On posterior being a function of the prior
| Type | Journal Article |
|---|---|
| Author | Lewis B. Sheiner |
| Volume | 61 |
| Pages | 275-291 |
| Publication | Clin Pharm Ther |
| Date | 1997 |
| Extra | Citation Key: she97lea tex.citeulike-article-id= 13264844 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
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
| Type | Journal Article |
|---|---|
| Author | Peter H. Westfall |
| Author | Wesley O. Johnson |
| Author | Jessica M. Utts |
| Volume | 84 |
| Pages | 419-427 |
| Publication | Biometrika |
| Date | 1997 |
| Extra | Citation Key: wes97bay tex.citeulike-article-id= 13265041 tex.posted-at= 2014-07-14 14:09:48 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
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
| Type | Book Section |
|---|---|
| Author | J. Brophy |
| Author | L. Joseph |
| Editor | D. Berry |
| Editor | D. Stangl |
| Place | New York |
| Publisher | Marcel Dekker |
| Pages | 83-104 |
| Date | 2000 |
| Extra | Citation Key: bro00bay tex.citeulike-article-id= 13265203 tex.posted-at= 2014-07-14 14:09:51 tex.priority= 0 |
| 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 |
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.
| Type | Journal Article |
|---|---|
| Author | Anthony O'Hagan |
| Author | John W. Stevens |
| Author | Jacques Montmartin |
| URL | http://dx.doi.org/10.1002/sim.861 |
| Volume | 20 |
| Pages | 733-753 |
| Publication | Stat Med |
| Date | 2001 |
| 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 |
| DOI | 10.1002/sim.861 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
winbugs example for getting probability of dominance
| Type | Journal Article |
|---|---|
| Author | Tomoyuki Kakizume |
| Author | Fanghong Zhang |
| Author | Yohei Kawasaki |
| Author | Takashi Daimon |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1971 |
| Rights | © 2019 John Wiley & Sons, Ltd. |
| Volume | 0 |
| Issue | 0 |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2019 |
| DOI | 10.1002/pst.1971 |
| Accessed | 9/15/2019, 5:25:49 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 9/15/2019, 5:25:49 PM |
| Modified | 9/15/2019, 5:26:23 PM |
| Type | Journal Article |
|---|---|
| Author | Julian PT Higgins |
| Author | David J. Spiegelhalter |
| URL | https://academic.oup.com/ije/article/31/1/96/655931 |
| Volume | 31 |
| Issue | 1 |
| Pages | 96-104 |
| Publication | International Journal of Epidemiology |
| ISSN | 0300-5771 |
| Date | 2002/02/01 |
| Journal Abbr | Int J Epidemiol |
| DOI | 10.1093/ije/31.1.96 |
| Accessed | 9/7/2019, 1:08:44 PM |
| Library Catalog | academic.oup.com |
| Language | en |
| Abstract | Abstract. Background There has been extensive discussion of the apparent conflict between meta-analyses and a mega-trial investigating the benefits of intraven |
| Short Title | Being sceptical about meta-analyses |
| Date Added | 9/7/2019, 1:08:44 PM |
| Modified | 9/7/2019, 1:09:23 PM |
magnesium
| Type | Journal Article |
|---|---|
| Author | Pierre Baldi |
| Author | Babak Shahbaba |
| URL | https://doi.org/10.1080/00031305.2019.1647876 |
| Volume | 0 |
| Issue | 0 |
| Pages | 1-9 |
| Publication | The American Statistician |
| ISSN | 0003-1305 |
| Date | August 12, 2019 |
| DOI | 10.1080/00031305.2019.1647876 |
| Accessed | 8/30/2019, 11:14:58 AM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 8/30/2019, 11:14:58 AM |
| Modified | 8/30/2019, 11:15:43 AM |
| Type | Journal Article |
|---|---|
| Author | James Berger |
| Author | L. Mark Berliner |
| URL | https://www.jstor.org/stable/2241230 |
| Volume | 14 |
| Issue | 2 |
| Pages | 461-486 |
| Publication | The Annals of Statistics |
| ISSN | 0090-5364 |
| Date | 1986 |
| Accessed | 8/23/2019, 8:51:45 AM |
| Library Catalog | JSTOR |
| 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. |
| Archive | JSTOR |
| Date Added | 8/23/2019, 8:51:46 AM |
| Modified | 8/23/2019, 8:52:18 AM |
| Type | Journal Article |
|---|---|
| Author | Daniel Kassler |
| Author | Ira Nichols-Barrer |
| Author | Mariel Finucane |
| URL | https://doi.org/10.1177/0193841X18818903 |
| Pages | 0193841X18818903 |
| Publication | Evaluation Review |
| ISSN | 0193-841X |
| Date | January 10, 2019 |
| Journal Abbr | Eval Rev |
| DOI | 10.1177/0193841X18818903 |
| Accessed | 8/13/2019, 9:31:24 AM |
| Library Catalog | SAGE Journals |
| Language | en |
| 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. |
| Short Title | Beyond “Treatment Versus Control” |
| Date Added | 8/13/2019, 9:31:24 AM |
| Modified | 8/13/2019, 9:32:26 AM |
| Type | Journal Article |
|---|---|
| Author | Adam J. King |
| Author | Robert E. Weiss |
| URL | https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxz029/5542991 |
| Publication | Biostatistics |
| Date | 2019 |
| Journal Abbr | Biostatistics |
| DOI | 10.1093/biostatistics/kxz029 |
| Accessed | 8/4/2019, 8:01:22 AM |
| Library Catalog | academic.oup.com |
| Language | en |
| 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 Added | 8/4/2019, 8:01:22 AM |
| Modified | 8/4/2019, 8:02:00 AM |
| Type | Journal Article |
|---|---|
| Author | Manuel Wiesenfarth |
| Author | Silvia Calderazzo |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13124 |
| Rights | This article is protected by copyright. All rights reserved. |
| Volume | 0 |
| Issue | ja |
| Publication | Biometrics |
| ISSN | 1541-0420 |
| Date | 2019 |
| DOI | 10.1111/biom.13124 |
| Accessed | 7/31/2019, 3:34:40 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 7/31/2019, 3:34:40 PM |
| Modified | 7/31/2019, 3:36:43 PM |
| Type | Journal Article |
|---|---|
| Author | Annette Kopp‐Schneider |
| Author | Silvia Calderazzo |
| Author | Manuel Wiesenfarth |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.201800395 |
| Rights | © 2019 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim |
| Volume | 0 |
| Issue | 0 |
| Publication | Biometrical Journal |
| ISSN | 1521-4036 |
| Date | 2019 |
| DOI | 10.1002/bimj.201800395 |
| Accessed | 7/7/2019, 9:29:11 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 7/7/2019, 9:29:11 AM |
| Modified | 7/7/2019, 9:30:26 AM |
| Type | Journal Article |
|---|---|
| Author | Yifan Zhang |
| Author | Lorenzo Trippa |
| Author | Giovanni Parmigiani |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8279 |
| Rights | © 2019 John Wiley & Sons, Ltd. |
| Volume | 0 |
| Issue | 0 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2019 |
| DOI | 10.1002/sim.8279 |
| Accessed | 6/20/2019, 8:01:10 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 6/20/2019, 8:01:10 AM |
| Modified | 6/20/2019, 8:02:01 AM |
| 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 |
| URL | https://doi.org/10.1080/01621459.2019.1604359 |
| Volume | 0 |
| Issue | 0 |
| Pages | 1-28 |
| Publication | Journal of the American Statistical Association |
| ISSN | 0162-1459 |
| Date | April 23, 2019 |
| DOI | 10.1080/01621459.2019.1604359 |
| Accessed | 6/20/2019, 7:35:05 AM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 6/20/2019, 7:35:05 AM |
| Modified | 6/20/2019, 7:37:05 AM |
| Type | Journal Article |
|---|---|
| Author | Julian Stander |
| Author | Luciana Dalla Valle |
| Author | Charlotte Taglioni |
| Author | Brunero Liseo |
| Author | Angie Wade |
| Author | Mario Cortina‐Borja |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8176 |
| Rights | © 2019 John Wiley & Sons, Ltd. |
| Volume | 0 |
| Issue | 0 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2019 |
| DOI | 10.1002/sim.8176 |
| Accessed | 6/1/2019, 10:10:58 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 6/1/2019, 10:10:58 AM |
| Modified | 6/1/2019, 10:11:43 AM |
| Type | Journal Article |
|---|---|
| Author | Andrew Gelman |
| Author | Ben Goodrich |
| Author | Jonah Gabry |
| Author | Aki Vehtari |
| URL | https://doi.org/10.1080/00031305.2018.1549100 |
| Volume | 0 |
| Issue | 0 |
| Pages | 1-7 |
| Publication | The American Statistician |
| ISSN | 0003-1305 |
| Date | December 10, 2018 |
| DOI | 10.1080/00031305.2018.1549100 |
| Accessed | 5/17/2019, 8:42:28 AM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 5/17/2019, 8:42:28 AM |
| Modified | 5/17/2019, 8:43:19 AM |
| Type | Journal Article |
|---|---|
| Author | Ban Kheng Tan |
| Author | Anastasios Panagiotelis |
| Author | George Athanasopoulos |
| URL | https://doi.org/10.1080/10618600.2018.1482765 |
| Volume | 28 |
| Issue | 1 |
| Pages | 155-173 |
| Publication | Journal of Computational and Graphical Statistics |
| ISSN | 1061-8600 |
| Date | January 2, 2019 |
| DOI | 10.1080/10618600.2018.1482765 |
| Accessed | 4/25/2019, 10:03:09 AM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 4/25/2019, 10:03:09 AM |
| Modified | 4/25/2019, 10:03:45 AM |
| Type | Journal Article |
|---|---|
| Author | Kan Li |
| Author | Sheng Luo |
| Author | Sammy Yuan |
| Author | Shahrul Mt‐Isa |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8166 |
| Rights | © 2019 John Wiley & Sons, Ltd. |
| Volume | 0 |
| Issue | 0 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2019 |
| DOI | 10.1002/sim.8166 |
| Accessed | 4/16/2019, 9:06:38 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 4/16/2019, 9:06:38 AM |
| Modified | 4/16/2019, 9:09:18 AM |
| Type | Journal Article |
|---|---|
| Author | Rubén Loaiza-Maya |
| Author | Michael Stanley Smith |
| URL | https://doi.org/10.1080/10618600.2018.1562936 |
| Volume | 0 |
| Issue | 0 |
| Pages | 1-17 |
| Publication | Journal of Computational and Graphical Statistics |
| ISSN | 1061-8600 |
| Date | January 14, 2019 |
| DOI | 10.1080/10618600.2018.1562936 |
| Accessed | 4/10/2019, 9:13:07 AM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 4/10/2019, 9:13:07 AM |
| Modified | 4/10/2019, 9:13:37 AM |
| Type | Journal Article |
|---|---|
| Author | Gaelle Saint‐Hilary |
| Author | Valentine Barboux |
| Author | Matthieu Pannaux |
| Author | Mauro Gasparini |
| Author | Veronique Robert |
| Author | Gianluca Mastrantonio |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8060 |
| Rights | © 2018 John Wiley & Sons, Ltd. |
| Volume | 38 |
| Issue | 10 |
| Pages | 1753-1774 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2019 |
| DOI | 10.1002/sim.8060 |
| Accessed | 4/3/2019, 9:04:33 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 4/3/2019, 9:04:33 AM |
| Modified | 4/3/2019, 9:05:21 AM |
| 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 |
| URL | https://onlinelibrary.wiley.com/doi/full/10.1002/pst.1941 |
| Volume | 0 |
| Issue | 0 |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1604 |
| Date | April 1, 2019 |
| Journal Abbr | Pharmaceutical Statistics |
| DOI | 10.1002/pst.1941 |
| Accessed | 4/2/2019, 9:26:53 AM |
| Library Catalog | onlinelibrary.wiley.com (Atypon) |
| 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. |
| Short Title | Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework |
| Date Added | 4/2/2019, 9:26:54 AM |
| Modified | 4/2/2019, 9:27:59 AM |
| 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 |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1906 |
| Rights | © 2018 John Wiley & Sons, Ltd. |
| Volume | 18 |
| Issue | 1 |
| Pages | 39-53 |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2019 |
| DOI | 10.1002/pst.1906 |
| Accessed | 1/26/2019, 12:14:39 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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. |
| Short Title | Applications of Bayesian statistical methodology to clinical trial design |
| Date Added | 1/26/2019, 12:14:39 PM |
| Modified | 1/26/2019, 12:15:14 PM |
| Type | Journal Article |
|---|---|
| Author | Trevelyan J. McKinley |
| Author | Michelle Morters |
| Author | James L. N. Wood |
| URL | https://projecteuclid.org/euclid.ba/1422468421 |
| Volume | 10 |
| Issue | 1 |
| Pages | 1-30 |
| Publication | Bayesian Analysis |
| ISSN | 1936-0975, 1931-6690 |
| Date | 2015-03 |
| Extra | MR: MR3420895 Zbl: 1334.62141 |
| Journal Abbr | Bayesian Anal. |
| DOI | 10.1214/14-BA884 |
| Accessed | 1/19/2019, 8:07:15 AM |
| Library Catalog | Project Euclid |
| Language | EN |
| 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 Added | 1/19/2019, 8:07:15 AM |
| Modified | 1/19/2019, 8:07:57 AM |
| Type | Journal Article |
|---|---|
| Author | Torrin M. Liddell |
| Author | John K. Kruschke |
| URL | http://www.sciencedirect.com/science/article/pii/S0022103117307746 |
| Volume | 79 |
| Pages | 328-348 |
| Publication | Journal of Experimental Social Psychology |
| ISSN | 0022-1031 |
| Date | November 1, 2018 |
| Journal Abbr | Journal of Experimental Social Psychology |
| DOI | 10.1016/j.jesp.2018.08.009 |
| Accessed | 1/7/2019, 1:55:52 PM |
| Library Catalog | ScienceDirect |
| 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. |
| Short Title | Analyzing ordinal data with metric models |
| Date Added | 1/7/2019, 1:55:52 PM |
| Modified | 1/7/2019, 1:56:25 PM |
| Type | Journal Article |
|---|---|
| Author | Paul-Christian Bürkner |
| Author | Matti Vuorre |
| URL | https://psyarxiv.com/x8swp/ |
| Date | 2018-02-28T23:41:26.334Z |
| DOI | 10.31234/osf.io/x8swp |
| Accessed | 1/7/2019, 1:52:26 PM |
| Library Catalog | psyarxiv.com |
| 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. |
| Short Title | Ordinal Regression Models in Psychology |
| Date Added | 1/7/2019, 1:52:26 PM |
| Modified | 1/7/2019, 1:54:01 PM |
| 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 |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1906 |
| Rights | © 2018 John Wiley & Sons, Ltd. |
| Volume | 0 |
| Issue | 0 |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2018 |
| DOI | 10.1002/pst.1906 |
| Accessed | 10/16/2018, 3:02:59 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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. |
| Short Title | Applications of Bayesian statistical methodology to clinical trial design |
| Date Added | 10/16/2018, 3:02:59 PM |
| Modified | 10/16/2018, 3:03:31 PM |
| Type | Journal Article |
|---|---|
| Author | Ban Kheng Tan |
| Author | Anastasios Panagiotelis |
| Author | George Athanasopoulos |
| URL | https://doi.org/10.1080/10618600.2018.1482765 |
| Volume | 0 |
| Issue | 0 |
| Pages | 1-19 |
| Publication | Journal of Computational and Graphical Statistics |
| ISSN | 1061-8600 |
| Date | June 11, 2018 |
| DOI | 10.1080/10618600.2018.1482765 |
| Accessed | 9/8/2018, 3:13:53 PM |
| Library Catalog | Taylor and Francis+NEJM |
| 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 Added | 9/8/2018, 3:13:53 PM |
| Modified | 9/8/2018, 3:14:28 PM |
| Type | Journal Article |
|---|---|
| Author | Kristina Weber |
| Author | Rob Hemmings |
| Author | Armin Koch |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1862 |
| Rights | © 2018 The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd. |
| Volume | 17 |
| Issue | 4 |
| Pages | 329-341 |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2018 |
| DOI | 10.1002/pst.1862 |
| Accessed | 7/13/2018, 11:34:41 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| 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 Added | 7/13/2018, 11:34:41 AM |
| Modified | 7/13/2018, 11:35:37 AM |
| Type | Journal Article |
|---|---|
| Author | Giorgio Paulon |
| Author | Maria De Iorio |
| Author | Alessandra Guglielmi |
| Author | Francesca Ieva |
| URL | https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxy026/5050476 |
| Publication | Biostatistics |
| Date | 2018 |
| Journal Abbr | Biostatistics |
| DOI | 10.1093/biostatistics/kxy026 |
| Accessed | 7/8/2018, 7:46:22 AM |
| Library Catalog | academic.oup.com |
| Language | en |
| 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. |
| Short Title | Joint modeling of recurrent events and survival |
| Date Added | 7/8/2018, 8:12:45 AM |
| Modified | 7/8/2018, 8:25:31 AM |