Item Type | Journal Article |
---|---|
Author | Xiang Zhou |
Author | Jerome P. Reiter |
Abstract | This article is aimed at practitioners who plan to use Bayesian inference on multiply-imputed datasets in settings where posterior distributions of the parameters of interest are not approximately Gaussian. We seek to steer practitioners away from a naive approach to Bayesian inference, namely estimating the posterior distribution in each completed dataset and averaging functionals of these distributions. We demonstrate that this approach results in unreliable inferences. A better approach is to mix draws from the posterior distributions from each completed dataset, and use the mixed draws to summarize the posterior distribution. Using simulations, we show that for this second approach to work well, the number of imputed datasets should be large. In particular, five to ten imputed datasets—which is the standard recommendation for multiple imputation—is generally not enough to result in reliable Bayesian inferences. |
Date | 2012 |
Library Catalog | Taylor and Francis+NEJM |
URL | https://doi.org/10.1198/tast.2010.09109 |
Accessed | 11/10/2023, 9:13:11 AM |
Extra | Publisher: Taylor & Francis _eprint: https://doi.org/10.1198/tast.2010.09109 |
Volume | 64 |
Pages | 159-163 |
Publication | The American Statistician |
DOI | 10.1198/tast.2010.09109 |
Issue | 2 |
ISSN | 0003-1305 |
Date Added | 11/10/2023, 9:13:11 AM |
Modified | 11/10/2023, 9:17:28 AM |
Item Type | Journal Article |
---|---|
Author | Angela M. Wood |
Author | Ian R. White |
Author | Patrick Royston |
Abstract | Abstract Multiple imputation is a popular technique for analysing incomplete data. Given the imputed data and a particular model, Rubin's rules (RR) for estimating parameters and standard errors are well established. However, there are currently no guidelines for variable selection in multiply imputed data sets. The usual practice is to perform variable selection amongst the complete cases, a simple but inefficient and potentially biased procedure. Alternatively, variable selection can be performed by repeated use of RR, which is more computationally demanding. An approximation can be obtained by a simple ‘stacked’ method that combines the multiply imputed data sets into one and uses a weighting scheme to account for the fraction of missing data in each covariate. We compare these and other approaches using simulations based around a trial in community psychiatry. Most methods improve on the naïve complete‐case analysis for variable selection, but importantly the type 1 error is only preserved if selection is based on RR, which is our recommended approach. Copyright © 2008 John Wiley & Sons, Ltd. |
Date | 2008-07-30 |
Language | en |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.3177 |
Accessed | 12/9/2023, 3:01:04 PM |
Volume | 27 |
Pages | 3227-3246 |
Publication | Statistics in Medicine |
DOI | 10.1002/sim.3177 |
Issue | 17 |
Journal Abbr | Statistics in Medicine |
ISSN | 0277-6715, 1097-0258 |
Date Added | 12/9/2023, 3:01:04 PM |
Modified | 12/9/2023, 3:03:47 PM |
Item Type | Journal Article |
---|---|
Author | Ahmad Hakeem Abdul Wahab |
Author | Yongming Qu |
Author | Hege Michiels |
Author | Junxiang Luo |
Author | Run Zhuang |
Author | Dominique McDaniel |
Author | Dong Xi |
Author | Elena Polverejan |
Author | Steven Gilbert |
Author | Stephen Ruberg |
Author | Arman Sabbaghi |
Abstract | Abstract Although clinical trials are often designed with randomization and well‐controlled protocols, complications will inevitably arise in the presence of intercurrent events (ICEs) such as treatment discontinuation. These can lead to missing outcome data and possibly confounding causal inference when the missingness is a function of a latent stratification of patients defined by intermediate outcomes. The pharmaceutical industry has been focused on developing new methods that can yield pertinent causal inferences in trials with ICEs. However, it is difficult to compare the properties of different methods developed in this endeavor as real‐life clinical trial data cannot be easily shared to provide benchmark data sets. Furthermore, different methods consider distinct assumptions for the underlying data‐generating mechanisms, and simulation studies often are customized to specific situations or methods. We develop a novel, general simulation model and corresponding Shiny application in R for clinical trials with ICEs, aptly named the Clinical Trials with Intercurrent Events Simulator (CITIES). It is formulated under the Rubin Causal Model where the considered treatment effects account for ICEs in clinical trials with repeated measures. CITIES facilitates the effective generation of data that resemble real‐life clinical trials with respect to their reported summary statistics, without requiring the use of the original trial data. We illustrate the utility of CITIES via two case studies involving real‐life clinical trials that demonstrate how CITIES provides a comprehensive tool for practitioners in the pharmaceutical industry to compare methods for the analysis of clinical trials with ICEs on identical, benchmark settings that resemble real‐life trials. |
Date | 2023-09-22 |
Language | en |
Short Title | CITIES |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/bimj.202200103 |
Accessed | 12/9/2023, 11:25:50 AM |
Pages | 2200103 |
Publication | Biometrical Journal |
DOI | 10.1002/bimj.202200103 |
Journal Abbr | Biometrical J |
ISSN | 0323-3847, 1521-4036 |
Date Added | 12/9/2023, 11:25:50 AM |
Modified | 12/9/2023, 11:26:33 AM |
Item Type | Journal Article |
---|---|
Author | Sara Van Erp |
Author | Daniel L. Oberski |
Author | Joris Mulder |
Date | 04/2019 |
Language | en |
Library Catalog | DOI.org (Crossref) |
URL | https://linkinghub.elsevier.com/retrieve/pii/S0022249618300567 |
Accessed | 12/11/2023, 11:20:44 AM |
Volume | 89 |
Pages | 31-50 |
Publication | Journal of Mathematical Psychology |
DOI | 10.1016/j.jmp.2018.12.004 |
Journal Abbr | Journal of Mathematical Psychology |
ISSN | 00222496 |
Date Added | 12/11/2023, 11:20:44 AM |
Modified | 12/11/2023, 11:21:23 AM |
Item Type | Journal Article |
---|---|
Author | James F. Troendle |
Author | Eric S. Leifer |
Author | Song Yang |
Author | Neal Jeffries |
Author | Dong‐Yun Kim |
Author | Jungnam Joo |
Author | Christopher M. O'Connor |
Abstract | Consider the choice of outcome for overall treatment benefit in a clinical trial which measures the first time to each of several clinical events. We describe several new variants of the win ratio that incorporate the time spent in each clinical state over the common follow‐up, where clinical state means the worst clinical event that has occurred by that time. One version allows restriction so that death during follow‐up is most important, while time spent in other clinical states is still accounted for. Three other variants are described; one is based on the average pairwise win time, one creates a continuous outcome for each participant based on expected win times against a reference distribution and another that uses the estimated distributions of clinical state to compare the treatment arms. Finally, a combination testing approach is described to give robust power for detecting treatment benefit across a broad range of alternatives. These new methods are designed to be closer to the overall treatment benefit/harm from a patient's perspective, compared to the ordinary win ratio. The new methods are compared to the composite event approach and the ordinary win ratio. Simulations show that when overall treatment benefit on death is substantial, the variants based on either the participants' expected win times (EWTs) against a reference distribution or estimated clinical state distributions have substantially higher power than either the pairwise comparison or composite event methods. The methods are illustrated by re‐analysis of the trial heart failure: a controlled trial investigating outcomes of exercise training. |
Date | 2024-02-28 |
Language | en |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.10045 |
Accessed | 3/3/2024, 4:05:52 PM |
Pages | sim.10045 |
Publication | Statistics in Medicine |
DOI | 10.1002/sim.10045 |
Journal Abbr | Statistics in Medicine |
ISSN | 0277-6715, 1097-0258 |
Date Added | 3/3/2024, 4:05:52 PM |
Modified | 3/3/2024, 4:06:39 PM |
Item Type | Journal Article |
---|---|
Author | Alice-Maria Toader |
Author | Marion K. Campbell |
Author | Jennifer K. Quint |
Author | Michael Robling |
Author | Matthew R Sydes |
Author | Joanna Thorn |
Author | Alexandra Wright-Hughes |
Author | Ly-Mee Yu |
Author | Tom. E. F. Abbott |
Author | Simon Bond |
Author | Fergus J. Caskey |
Author | Madeleine Clout |
Author | Michelle Collinson |
Author | Bethan Copsey |
Author | Gwyneth Davies |
Author | Timothy Driscoll |
Author | Carrol Gamble |
Author | Xavier L. Griffin |
Author | Thomas Hamborg |
Author | Jessica Harris |
Author | David A. Harrison |
Author | Deena Harji |
Author | Emily J. Henderson |
Author | Pip Logan |
Author | Sharon B. Love |
Author | Laura A. Magee |
Author | Alastair O’Brien |
Author | Maria Pufulete |
Author | Padmanabhan Ramnarayan |
Author | Athanasios Saratzis |
Author | Jo Smith |
Author | Ivonne Solis-Trapala |
Author | Clive Stubbs |
Author | Amanda Farrin |
Author | Paula Williamson |
Abstract | Abstract Background Healthcare system data (HSD) are increasingly used in clinical trials, augmenting or replacing traditional methods of collecting outcome data. This study, PRIMORANT, set out to identify, in the UK context, issues to be considered before the decision to use HSD for outcome data in a clinical trial is finalised, a methodological question prioritised by the clinical trials community. Methods The PRIMORANT study had three phases. First, an initial workshop was held to scope the issues faced by trialists when considering whether to use HSDs for trial outcomes. Second, a consultation exercise was undertaken with clinical trials unit (CTU) staff, trialists, methodologists, clinicians, funding panels and data providers. Third, a final discussion workshop was held, at which the results of the consultation were fed back, case studies presented, and issues considered in small breakout groups. Results Key topics included in the consultation process were the validity of outcome data, timeliness of data capture, internal pilots, data-sharing, practical issues, and decision-making. A majority of consultation respondents ( n = 78, 95%) considered the development of guidance for trialists to be feasible. Guidance was developed following the discussion workshop, for the five broad areas of terminology, feasibility, internal pilots, onward data sharing, and data archiving. Conclusions We provide guidance to inform decisions about whether or not to use HSDs for outcomes, and if so, to assist trialists in working with registries and other HSD providers to improve the design and delivery of trials. |
Date | 2024-01-29 |
Language | en |
Short Title | Using healthcare systems data for outcomes in clinical trials |
Library Catalog | DOI.org (Crossref) |
URL | https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-024-07926-z |
Accessed | 2/5/2024, 4:27:41 PM |
Volume | 25 |
Pages | 94 |
Publication | Trials |
DOI | 10.1186/s13063-024-07926-z |
Issue | 1 |
Journal Abbr | Trials |
ISSN | 1745-6215 |
Date Added | 2/5/2024, 4:27:41 PM |
Modified | 2/5/2024, 4:28:10 PM |
Item Type | Journal Article |
---|---|
Author | Zequn Sun |
Author | Dongjun Chung |
Author | Brian Neelon |
Author | Andrew Millar‐Wilson |
Author | Stephen P. Ethier |
Author | Feifei Xiao |
Author | Yinan Zheng |
Author | Kristin Wallace |
Author | Gary Hardiman |
Abstract | In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi‐omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene‐level analysis and lack the ability to facilitate biological findings at the pathway‐level. In this article, we propose Bayes‐InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway‐guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes‐InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya‐Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package “INGRID” implementing the proposed approach is currently available in our research group GitHub webpage ( https://dongjunchung.github.io/INGRID/ ). |
Date | 2023-12-10 |
Language | en |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.9911 |
Accessed | 12/9/2023, 11:36:41 AM |
Volume | 42 |
Pages | 5266-5284 |
Publication | Statistics in Medicine |
DOI | 10.1002/sim.9911 |
Issue | 28 |
Journal Abbr | Statistics in Medicine |
ISSN | 0277-6715, 1097-0258 |
Date Added | 12/9/2023, 11:36:41 AM |
Modified | 12/9/2023, 11:37:43 AM |
Item Type | Journal Article |
---|---|
Author | Satya Prakash Singh |
Abstract | Abstract Recently, there has been a growing interest in designing cluster trials using stepped wedge design (SWD). An SWD is a type of cluster–crossover design in which clusters of individuals are randomized unidirectional from a control to an intervention at certain time points. The intraclass correlation coefficient (ICC) that measures the dependency of subject within a cluster plays an important role in design and analysis of stepped wedge trials. In this paper, we discuss a Bayesian approach to address the dependency of SWD on the ICC and robust Bayesian SWDs are proposed. Bayesian design is shown to be more robust against the misspecification of the parameter values compared to the locally optimal design. Designs are obtained for the various choices of priors assigned to the ICC. A detailed sensitivity analysis is performed to assess the robustness of proposed optimal designs. The power superiority of Bayesian design against the commonly used balanced design is demonstrated numerically using hypothetical as well as real scenarios. |
Date | 2023-12-06 |
Language | en |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/bimj.202300168 |
Accessed | 12/9/2023, 10:55:57 AM |
Pages | 2300168 |
Publication | Biometrical Journal |
DOI | 10.1002/bimj.202300168 |
Journal Abbr | Biometrical J |
ISSN | 0323-3847, 1521-4036 |
Date Added | 12/9/2023, 10:55:57 AM |
Modified | 12/9/2023, 10:56:38 AM |
Item Type | Journal Article |
---|---|
Author | Juned Siddique |
Author | Michael J. Daniels |
Author | Gül Inan |
Author | Samuel Battalio |
Author | Bonnie Spring |
Author | Donald Hedeker |
Abstract | Physical activity (PA) guidelines recommend that PA be accumulated in bouts of 10 minutes or more in duration. Recently, researchers have sought to better understand how participants in PA interventions increase their activity. Participants can increase their daily PA by increasing the number of PA bouts per day while keeping the duration of the bouts constant; they can keep the number of bouts constant but increase the duration of each bout; or participants can increase both the number of bouts and their duration. We propose a novel joint modeling framework for modeling PA bouts and their duration over time. Our joint model is comprised of two sub‐models: a mixed‐effects Poisson hurdle sub‐model for the number of bouts per day and a mixed‐effects location scale gamma regression sub‐model to characterize the duration of the bouts and their variance. The model allows us to estimate how daily PA bouts and their duration vary together over the course of an intervention and by treatment condition and is specifically designed to capture the unique distributional features of bouted PA as measured by accelerometer: frequent measurements, zero‐inflated bouts, and skewed bout durations. We apply our methods to the Make Better Choices study, a longitudinal lifestyle intervention trial to increase PA. We perform a simulation study to evaluate how well our model is able to estimate relationships between outcomes. |
Date | 2023-12-10 |
Language | en |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.9903 |
Accessed | 12/9/2023, 11:33:12 AM |
Volume | 42 |
Pages | 5100-5112 |
Publication | Statistics in Medicine |
DOI | 10.1002/sim.9903 |
Issue | 28 |
Journal Abbr | Statistics in Medicine |
ISSN | 0277-6715, 1097-0258 |
Date Added | 12/9/2023, 11:33:12 AM |
Modified | 12/9/2023, 11:34:02 AM |
Joint model for frequency of physical activity and duration of each episode
Item Type | Journal Article |
---|---|
Author | Richard D Riley |
Author | Lucinda Archer |
Author | Kym I E Snell |
Author | Joie Ensor |
Author | Paula Dhiman |
Author | Glen P Martin |
Author | Laura J Bonnett |
Author | Gary S Collins |
Date | 2024-01-15 |
Language | en |
Short Title | Evaluation of clinical prediction models (part 2) |
Library Catalog | DOI.org (Crossref) |
URL | https://www.bmj.com/lookup/doi/10.1136/bmj-2023-074820 |
Accessed | 1/20/2024, 9:39:54 AM |
Pages | e074820 |
Publication | BMJ |
DOI | 10.1136/bmj-2023-074820 |
Journal Abbr | BMJ |
ISSN | 1756-1833 |
Date Added | 1/20/2024, 9:39:54 AM |
Modified | 1/20/2024, 9:41:01 AM |
Item Type | Journal Article |
---|---|
Author | Richard D Riley |
Author | Kym I E Snell |
Author | Lucinda Archer |
Author | Joie Ensor |
Author | Thomas P A Debray |
Author | Ben Van Calster |
Author | Maarten Van Smeden |
Author | Gary S Collins |
Date | 2024-01-22 |
Language | en |
Short Title | Evaluation of clinical prediction models (part 3) |
Library Catalog | DOI.org (Crossref) |
URL | https://www.bmj.com/lookup/doi/10.1136/bmj-2023-074821 |
Accessed | 1/23/2024, 3:11:30 PM |
Pages | e074821 |
Publication | BMJ |
DOI | 10.1136/bmj-2023-074821 |
Journal Abbr | BMJ |
ISSN | 1756-1833 |
Date Added | 1/23/2024, 3:11:30 PM |
Modified | 1/23/2024, 3:12:01 PM |
Item Type | Journal Article |
---|---|
Author | Fabio Rigat |
Abstract | Abstract Prior probabilities of clinical hypotheses are not systematically used for clinical trial design yet, due to a concern that poor priors may lead to poor decisions. To address this concern, a conservative approach to Bayesian trial design is illustrated here, requiring that the operational characteristics of the primary trial outcome are stronger than the prior. This approach is complementary to current Bayesian design methods, in that it insures against prior‐data conflict by defining a sample size commensurate to a discrete design prior. This approach is ethical, in that it requires designs appropriate to achieving pre‐specified levels of clinical equipoise imbalance. Practical examples are discussed, illustrating design of trials with binary or time to event endpoints. Moderate increases in phase II study sample size are shown to deliver strong levels of overall evidence for go/no‐go clinical development decisions. Levels of negative evidence provided by group sequential confirmatory designs are found negligible, highlighting the importance of complementing efficacy boundaries with non‐binding futility criteria. |
Date | 2023-09-26 |
Language | en |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/pst.2339 |
Accessed | 12/9/2023, 11:18:48 AM |
Pages | pst.2339 |
Publication | Pharmaceutical Statistics |
DOI | 10.1002/pst.2339 |
Journal Abbr | Pharmaceutical Statistics |
ISSN | 1539-1604, 1539-1612 |
Date Added | 12/9/2023, 11:18:48 AM |
Modified | 12/9/2023, 11:19:27 AM |
Includes some sample size considerations to ensure that the prior is not too impactful
Item Type | Journal Article |
---|---|
Author | Hongxiang Qiu |
Author | Andrea J. Cook |
Author | Jennifer F. Bobb |
Abstract | Generalized linear mixed models (GLMM) are commonly used to analyze clustered data, but when the number of clusters is small to moderate, standard statistical tests may produce elevated type I error rates. Small‐sample corrections have been proposed for continuous or binary outcomes without covariate adjustment. However, appropriate tests to use for count outcomes or under covariate‐adjusted models remains unknown. An important setting in which this issue arises is in cluster‐randomized trials (CRTs). Because many CRTs have just a few clusters (eg, clinics or health systems), covariate adjustment is particularly critical to address potential chance imbalance and/or low power (eg, adjustment following stratified randomization or for the baseline value of the outcome). We conducted simulations to evaluate GLMM‐based tests of the treatment effect that account for the small (10) or moderate (20) number of clusters under a parallel‐group CRT setting across scenarios of covariate adjustment (including adjustment for one or more person‐level or cluster‐level covariates) for both binary and count outcomes. We find that when the intraclass correlation is non‐negligible (0.01) and the number of covariates is small (2), likelihood ratio tests with a between‐within denominator degree of freedom have type I error rates close to the nominal level. When the number of covariates is moderate (5), across our simulation scenarios, the relative performance of the tests varied considerably and no method performed uniformly well. Therefore, we recommend adjusting for no more than a few covariates and using likelihood ratio tests with a between‐within denominator degree of freedom. |
Date | 2023-11-07 |
Language | en |
Short Title | Evaluating tests for cluster‐randomized trials with few clusters under generalized linear mixed models with covariate adjustment |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.9950 |
Accessed | 12/9/2023, 11:02:51 AM |
Pages | sim.9950 |
Publication | Statistics in Medicine |
DOI | 10.1002/sim.9950 |
Journal Abbr | Statistics in Medicine |
ISSN | 0277-6715, 1097-0258 |
Date Added | 12/9/2023, 11:02:51 AM |
Modified | 12/9/2023, 11:03:29 AM |
Item Type | Journal Article |
---|---|
Author | Kimihiro Noguchi |
Author | Yulia R. Gel |
Author | Edgar Brunner |
Author | Frank Konietschke |
Date | 2012 |
Language | en |
Short Title | <b>nparLD</b> |
Library Catalog | DOI.org (Crossref) |
URL | http://www.jstatsoft.org/v50/i12/ |
Accessed | 1/23/2024, 3:02:15 PM |
Volume | 50 |
Publication | Journal of Statistical Software |
DOI | 10.18637/jss.v050.i12 |
Issue | 12 |
Journal Abbr | J. Stat. Soft. |
ISSN | 1548-7660 |
Date Added | 1/23/2024, 3:02:15 PM |
Modified | 1/23/2024, 3:02:55 PM |
Table 1 has formula for the concordance probability (probability index) for a proportional odds model when PO holds (continuous Y with a shift in location for a logistic distribution).
Item Type | Journal Article |
---|---|
Author | Dorien Neijzen |
Author | Gerton Lunter |
Abstract | We review popular unsupervised learning methods for the analysis of high‐dimensional data encountered in, for example, genomics, medical imaging, cohort studies, and biobanks. We show that four commonly used methods, principal component analysis, K‐means clustering, nonnegative matrix factorization, and latent Dirichlet allocation, can be written as probabilistic models underpinned by a low‐rank matrix factorization. In addition to highlighting their similarities, this formulation clarifies the various assumptions and restrictions of each approach, which eases identifying the appropriate method for specific applications for applied medical researchers. We also touch upon the most important aspects of inference and model selection for the application of these methods to health data. |
Date | 2023-10-18 |
Language | en |
Short Title | Unsupervised learning for medical data |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.9924 |
Accessed | 12/9/2023, 11:04:32 AM |
Pages | sim.9924 |
Publication | Statistics in Medicine |
DOI | 10.1002/sim.9924 |
Journal Abbr | Statistics in Medicine |
ISSN | 0277-6715, 1097-0258 |
Date Added | 12/9/2023, 11:04:32 AM |
Modified | 12/9/2023, 11:05:19 AM |
Item Type | Journal Article |
---|---|
Author | Tim P. Morris |
Author | Ian R. White |
Author | James R. Carpenter |
Author | Simon J. Stanworth |
Author | Patrick Royston |
Date | 2015-11-10 |
Language | en |
Short Title | Combining fractional polynomial model building with multiple imputation |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.6553 |
Accessed | 12/9/2023, 2:58:49 PM |
Volume | 34 |
Pages | 3298-3317 |
Publication | Statistics in Medicine |
DOI | 10.1002/sim.6553 |
Issue | 25 |
Journal Abbr | Statist. Med. |
ISSN | 02776715 |
Date Added | 12/9/2023, 2:58:49 PM |
Modified | 12/9/2023, 3:04:13 PM |
Item Type | Journal Article |
---|---|
Author | Guiomar Mendieta |
Author | Stuart Pocock |
Author | Virginia Mass |
Author | Andrea Moreno |
Author | Ruth Owen |
Author | Inés García-Lunar |
Author | Beatriz López-Melgar |
Author | Jose J. Fuster |
Author | Vicente Andres |
Author | Cristina Pérez-Herreras |
Author | Hector Bueno |
Author | Antonio Fernández-Ortiz |
Author | Javier Sanchez-Gonzalez |
Author | Ana García-Alvarez |
Author | Borja Ibáñez |
Author | Valentin Fuster |
Date | 11/2023 |
Language | en |
Library Catalog | DOI.org (Crossref) |
URL | https://linkinghub.elsevier.com/retrieve/pii/S0735109723076295 |
Accessed | 2/13/2024, 12:42:14 PM |
Volume | 82 |
Pages | 2069-2083 |
Publication | Journal of the American College of Cardiology |
DOI | 10.1016/j.jacc.2023.09.814 |
Issue | 22 |
Journal Abbr | Journal of the American College of Cardiology |
ISSN | 07351097 |
Date Added | 2/13/2024, 12:42:14 PM |
Modified | 2/13/2024, 12:42:33 PM |
Item Type | Journal Article |
---|---|
Author | Pavlos Mamouris |
Author | Vahid Nassiri |
Author | Geert Verbeke |
Author | Arne Janssens |
Author | Bert Vaes |
Author | Geert Molenberghs |
Abstract | Imputation of longitudinal categorical covariates with several waves and many predictors is cumbersome in terms of implausible transitions, colinearity, and overfitting. We designed a simulation study with data obtained from a general practitioners' morbidity registry in Belgium for three waves, with smoking as the longitudinal covariate of interest. We set varying proportions of data on smoking to missing completely at random and missing not at random with proportions of missingness equal to 10%, 30%, 50%, and 70%. This study proposed a 3‐stage approach that allows flexibility when imputing time‐dependent categorical covariates. First, multiple imputation using fully conditional specification or multiple imputation for the predictor variables was deployed using the wide format such that previous and future information of the same patient was utilized. Second, a joint Markov transition model for initial, forward, backward, and intermittent probabilities was developed for each imputed dataset. Finally, this transition model was used for imputation. We compared the performance of this methodology with an analyses of the complete data and with listwise deletion in terms of bias and root mean square error. Next, we applied this methodology in a clinical case for years 2017 to 2021, where we estimated the effect of several covariates on the pneumococcal vaccination. This methodological framework ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. Finally, a companion R package was developed to enable the replication and easy application of this methodology. |
Date | 2023-12-20 |
Language | en |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.9919 |
Accessed | 12/9/2023, 11:12:11 AM |
Volume | 42 |
Pages | 5405-5418 |
Publication | Statistics in Medicine |
DOI | 10.1002/sim.9919 |
Issue | 29 |
Journal Abbr | Statistics in Medicine |
ISSN | 0277-6715, 1097-0258 |
Date Added | 12/9/2023, 11:12:11 AM |
Modified | 12/9/2023, 11:12:59 AM |
Item Type | Journal Article |
---|---|
Author | Joseph N. Luchman |
Abstract | Determining independent variable relative importance is a highly useful practice in organizational science. Whereas techniques to determine independent variable importance are available for normally distributed and binary dependent variable models, such techniques have not been extended to multicategory dependent variables (MCDVs). The current work extends previous research on binary dependent variable relative importance analysis to provide a methodology for conducting relative importance analysis on MCDV models from a dominance analysis (DA) perspective. Moreover, the current work provides a set of comprehensive data analytic examples that demonstrate how and when to use MCDV models in a DA and the advantages general DA statistics offer in interpreting MCDV model results. Moreover, the current work outlines best practices for determining independent variable relative importance for MCDVs using replicable examples on data from the publicly available General Social Survey. The present work then contributes to the literature by using in-depth data analytic examples to outline best practices in conducting relative importance analysis for MCDV models and by highlighting unique information DA results provide about MCDV models. |
Date | 10/2014 |
Language | en |
Short Title | Relative Importance Analysis With Multicategory Dependent Variables |
Library Catalog | DOI.org (Crossref) |
URL | http://journals.sagepub.com/doi/10.1177/1094428114544509 |
Accessed | 12/10/2023, 8:20:53 AM |
Volume | 17 |
Pages | 452-471 |
Publication | Organizational Research Methods |
DOI | 10.1177/1094428114544509 |
Issue | 4 |
Journal Abbr | Organizational Research Methods |
ISSN | 1094-4281, 1552-7425 |
Date Added | 12/10/2023, 8:20:53 AM |
Modified | 12/10/2023, 8:21:41 AM |
Measures based on pseudo R^2 and considering all possible subsets of covariates. Good background section with review of pseudo R^2 measures.
Item Type | Journal Article |
---|---|
Author | Zihang Lu |
Author | Mojtaba Ahmadiankalati |
Author | Zhiwen Tan |
Abstract | Clustering longitudinal features is a common goal in medical studies to identify distinct disease developmental trajectories. Compared to clustering a single longitudinal feature, integrating multiple longitudinal features allows additional information to be incorporated into the clustering process, which may reveal co‐existing longitudinal patterns and generate deeper biological insight. Despite its increasing importance and popularity, there is limited practical guidance for implementing cluster analysis approaches for multiple longitudinal features and evaluating their comparative performance in medical datasets. In this paper, we provide an overview of several commonly used approaches to clustering multiple longitudinal features, with an emphasis on application and implementation through R software. These methods can be broadly categorized into two categories, namely model‐based (including frequentist and Bayesian) approaches and algorithm‐based approaches. To evaluate their performance, we compare these approaches using real‐life and simulated datasets. These results provide practical guidance to applied researchers who are interested in applying these approaches for clustering multiple longitudinal features. Recommendations for applied researchers and suggestions for future research in this area are also discussed. |
Date | 2023-12-20 |
Language | en |
Short Title | Joint clustering multiple longitudinal features |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.9917 |
Accessed | 12/9/2023, 11:09:24 AM |
Volume | 42 |
Pages | 5513-5540 |
Publication | Statistics in Medicine |
DOI | 10.1002/sim.9917 |
Issue | 29 |
Journal Abbr | Statistics in Medicine |
ISSN | 0277-6715, 1097-0258 |
Date Added | 12/9/2023, 11:09:24 AM |
Modified | 12/9/2023, 11:09:37 AM |
Item Type | Journal Article |
---|---|
Author | Haodong Li |
Author | Sonali Rosete |
Author | Jeremy Coyle |
Author | Rachael V. Phillips |
Author | Nima S. Hejazi |
Author | Ivana Malenica |
Author | Benjamin F. Arnold |
Author | Jade Benjamin‐Chung |
Author | Andrew Mertens |
Author | John M. Colford |
Author | Mark J. Van Der Laan |
Author | Alan E. Hubbard |
Abstract | Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targeted maximum likelihood estimation. There are even more recent augmentations of these procedures that can increase robustness, by adding a layer of cross‐validation (cross‐validated targeted maximum likelihood estimation and double machine learning, as applied to substitution and estimating equation approaches, respectively). While these methods have been evaluated individually on simulated and experimental data sets, a comprehensive analysis of their performance across real data based simulations have yet to be conducted. In this work, we benchmark multiple widely used methods for estimation of the average treatment effect using ten different nutrition intervention studies data. A nonparametric regression method, undersmoothed highly adaptive lasso, is used to generate the simulated distribution which preserves important features from the observed data and reproduces a set of true target parameters. For each simulated data, we apply the methods above to estimate the average treatment effects as well as their standard errors and resulting confidence intervals. Based on the analytic results, a general recommendation is put forth for use of the cross‐validated variants of both substitution and estimating equation estimators. We conclude that the additional layer of cross‐validation helps in avoiding unintentional over‐fitting of nuisance parameter functionals and leads to more robust inferences. |
Date | 2022-05-30 |
Language | en |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.9348 |
Accessed | 2/5/2024, 4:37:20 PM |
Volume | 41 |
Pages | 2132-2165 |
Publication | Statistics in Medicine |
DOI | 10.1002/sim.9348 |
Issue | 12 |
Journal Abbr | Statistics in Medicine |
ISSN | 0277-6715, 1097-0258 |
Date Added | 2/5/2024, 4:37:20 PM |
Modified | 2/5/2024, 4:37:50 PM |
Need to add an outer loop for targeted MLE to get correct standard errors in presence of overfitting
Item Type | Journal Article |
---|---|
Author | Kateline Le Bourdonnec |
Author | Cécilia Samieri |
Author | Christophe Tzourio |
Author | Thibault Mura |
Author | Aniket Mishra |
Author | David‐Alexandre Trégouët |
Author | Cécile Proust‐Lima |
Abstract | Abstract Instrumental variable methods, which handle unmeasured confounding by targeting the part of the exposure explained by an exogenous variable not subject to confounding, have gained much interest in observational studies. We consider the very frequent setting of estimating the unconfounded effect of an exposure measured at baseline on the subsequent trajectory of an outcome repeatedly measured over time. We didactically explain how to apply the instrumental variable method in such setting by adapting the two‐stage classical methodology with (1) the prediction of the exposure according to the instrumental variable, (2) its inclusion into a mixed model to quantify the exposure association with the subsequent outcome trajectory, and (3) the computation of the estimated total variance. A simulation study illustrates the consequences of unmeasured confounding in classical analyses and the usefulness of the instrumental variable approach. The methodology is then applied to 6224 participants of the 3C cohort to estimate the association of type‐2 diabetes with subsequent cognitive trajectory, using 42 genetic polymorphisms as instrumental variables. This contribution shows how to handle endogeneity when interested in repeated outcomes, along with a R implementation. However, it should still be used with caution as it relies on instrumental variable assumptions hardly testable in practice. |
Date | 2023-12-14 |
Language | en |
Short Title | Addressing unmeasured confounders in cohort studies |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/bimj.202200358 |
Accessed | 12/16/2023, 7:23:30 AM |
Pages | 2200358 |
Publication | Biometrical Journal |
DOI | 10.1002/bimj.202200358 |
Journal Abbr | Biometrical J |
ISSN | 0323-3847, 1521-4036 |
Date Added | 12/16/2023, 7:23:30 AM |
Modified | 12/16/2023, 7:24:06 AM |
Item Type | Journal Article |
---|---|
Author | Brennan C Kahan |
Author | Joanna Hindley |
Author | Mark Edwards |
Author | Suzie Cro |
Author | Tim P Morris |
Date | 2024-01-23 |
Language | en |
Short Title | The estimands framework |
Library Catalog | DOI.org (Crossref) |
URL | https://www.bmj.com/lookup/doi/10.1136/bmj-2023-076316 |
Accessed | 1/28/2024, 10:10:53 AM |
Pages | e076316 |
Publication | BMJ |
DOI | 10.1136/bmj-2023-076316 |
Journal Abbr | BMJ |
ISSN | 1756-1833 |
Date Added | 1/28/2024, 10:10:53 AM |
Modified | 1/28/2024, 10:11:42 AM |
Item Type | Journal Article |
---|---|
Author | Gabriele Infante |
Author | Rosalba Miceli |
Author | Federico Ambrogi |
Abstract | Prediction models are increasingly developed and used in diagnostic and prognostic studies, where the use of machine learning (ML) methods is becoming more and more popular over traditional regression techniques. For survival outcomes the Cox proportional hazards model is generally used and it has been proven to achieve good prediction performances with few strong covariates. The possibility to improve the model performance by including nonlinearities, covariate interactions and time‐varying effects while controlling for overfitting must be carefully considered during the model building phase. On the other hand, ML techniques are able to learn complexities from data at the cost of hyper‐parameter tuning and interpretability. One aspect of special interest is the sample size needed for developing a survival prediction model. While there is guidance when using traditional statistical models, the same does not apply when using ML techniques. This work develops a time‐to‐event simulation framework to evaluate performances of Cox regression compared, among others, to tuned random survival forest, gradient boosting, and neural networks at varying sample sizes. Simulations were based on replications of subjects from publicly available databases, where event times were simulated according to a Cox model with nonlinearities on continuous variables and time‐varying effects and on the SEER registry data. |
Date | 2023-11-10 |
Language | en |
Short Title | Sample size and predictive performance of machine learning methods with survival data |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.9931 |
Accessed | 11/11/2023, 9:23:00 AM |
Pages | sim.9931 |
Publication | Statistics in Medicine |
DOI | 10.1002/sim.9931 |
Journal Abbr | Statistics in Medicine |
ISSN | 0277-6715, 1097-0258 |
Date Added | 11/11/2023, 9:23:09 AM |
Modified | 11/11/2023, 9:23:09 AM |
Item Type | Journal Article |
---|---|
Author | Audinga‐Dea Hazewinkel |
Author | Kate Tilling |
Author | Kaitlin H. Wade |
Author | Tom Palmer |
Abstract | Abstract Randomized controlled trials (RCTs) are vulnerable to bias from missing data. When outcomes are missing not at random (MNAR), estimates from complete case analysis (CCA) and multiple imputation (MI) may be biased. There is no statistical test for distinguishing between outcomes missing at random (MAR) and MNAR. Current strategies rely on comparing dropout proportions and covariate distributions, and using auxiliary information to assess the likelihood of dropout being associated with the outcome. We propose using the observed variance difference across trial arms as a tool for assessing the risk of dropout being MNAR in RCTs with continuous outcomes. In an RCT, at randomization, the distributions of all covariates should be equal in the populations randomized to the intervention and control arms. Under the assumption of homogeneous treatment effects and homoskedastic outcome errors, the variance of the outcome will also be equal in the two populations over the course of follow‐up. We show that under MAR dropout, the observed outcome variances, conditional on the variables included in the model, are equal across trial arms, whereas MNAR dropout may result in unequal variances. Consequently, unequal observed conditional trial arm variances are an indicator of MNAR dropout and possible bias of the estimated treatment effect. Heterogeneous treatment effects or heteroskedastic outcome errors are another potential cause of observing different outcome variances. We show that for longitudinal data, we can isolate the effect of MNAR outcome‐dependent dropout by considering the variance difference at baseline in the same set of patients who are observed at final follow‐up. We illustrate our method in simulation for CCA and MI, and in applications using individual‐level data and summary data. |
Date | 2023-09-20 |
Language | en |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/bimj.202200116 |
Accessed | 12/9/2023, 11:27:56 AM |
Pages | 2200116 |
Publication | Biometrical Journal |
DOI | 10.1002/bimj.202200116 |
Journal Abbr | Biometrical J |
ISSN | 0323-3847, 1521-4036 |
Date Added | 12/9/2023, 11:27:56 AM |
Modified | 12/9/2023, 11:28:36 AM |
Indirect assessment of MNAR by comparing variances of continuous outcome variable. Not sure if authors discussed transformation assumptions about Y.
Item Type | Journal Article |
---|---|
Author | Gail Hayward |
Author | Ly-Mee Yu |
Author | Paul Little |
Author | Oghenekome Gbinigie |
Author | Milensu Shanyinde |
Author | Victoria Harris |
Author | Jienchi Dorward |
Author | Benjamin R Saville |
Author | Nicholas Berry |
Author | Philip H Evans |
Author | Nicholas Pb Thomas |
Author | Mahendra G Patel |
Author | Duncan Richards |
Author | Oliver Van Hecke |
Author | Michelle A Detry |
Author | Christina Saunders |
Author | Mark Fitzgerald |
Author | Jared Robinson |
Author | Charlotte Latimer-Bell |
Author | Julie Allen |
Author | Emma Ogburn |
Author | Jenna Grabey |
Author | Simon De Lusignan |
Author | Fd Richard Hobbs |
Author | Christopher C Butler |
Date | 2/2024 |
Language | en |
Short Title | Ivermectin for COVID-19 in adults in the community (PRINCIPLE) |
Library Catalog | DOI.org (Crossref) |
URL | https://linkinghub.elsevier.com/retrieve/pii/S0163445324000641 |
Accessed | 3/2/2024, 8:03:08 AM |
Pages | 106130 |
Publication | Journal of Infection |
DOI | 10.1016/j.jinf.2024.106130 |
Journal Abbr | Journal of Infection |
ISSN | 01634453 |
Date Added | 3/2/2024, 8:03:08 AM |
Modified | 3/2/2024, 8:04:03 AM |
Item Type | Journal Article |
---|---|
Author | Moritz Hanke |
Author | Louis Dijkstra |
Author | Ronja Foraita |
Author | Vanessa Didelez |
Abstract | Abstract We consider the question of variable selection in linear regressions, in the sense of identifying the correct direct predictors (those variables that have nonzero coefficients given all candidate predictors). Best subset selection (BSS) is often considered the “gold standard,” with its use being restricted only by its NP‐hard nature. Alternatives such as the least absolute shrinkage and selection operator (Lasso) or the Elastic net (Enet) have become methods of choice in high‐dimensional settings. A recent proposal represents BSS as a mixed‐integer optimization problem so that large problems have become computationally feasible. We present an extensive neutral comparison assessing the ability to select the correct direct predictors of BSS compared to forward stepwise selection (FSS), Lasso, and Enet. The simulation considers a range of settings that are challenging regarding dimensionality (number of observations and variables), signal‐to‐noise ratios, and correlations between predictors. As fair measure of performance, we primarily used the best possible F1‐score for each method, and results were confirmed by alternative performance measures and practical criteria for choosing the tuning parameters and subset sizes. Surprisingly, it was only in settings where the signal‐to‐noise ratio was high and the variables were uncorrelated that BSS reliably outperformed the other methods, even in low‐dimensional settings. Furthermore, FSS performed almost identically to BSS. Our results shed new light on the usual presumption of BSS being, in principle, the best choice for selecting the correct direct predictors. Especially for correlated variables, alternatives like Enet are faster and appear to perform better in practical settings. |
Date | 2023-08-29 |
Language | en |
Short Title | Variable selection in linear regression models |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/bimj.202200209 |
Accessed | 12/9/2023, 11:45:32 AM |
Pages | 2200209 |
Publication | Biometrical Journal |
DOI | 10.1002/bimj.202200209 |
Journal Abbr | Biometrical J |
ISSN | 0323-3847, 1521-4036 |
Date Added | 12/9/2023, 11:45:32 AM |
Modified | 12/9/2023, 11:46:10 AM |
Item Type | Journal Article |
---|---|
Author | John Gregson |
Author | Gregg W. Stone |
Author | Deepak L. Bhatt |
Author | Milton Packer |
Author | Stefan D. Anker |
Author | Cordula Zeller |
Author | Bjorn Redfors |
Author | Stuart J. Pocock |
Date | 10/2023 |
Language | en |
Library Catalog | DOI.org (Crossref) |
URL | https://linkinghub.elsevier.com/retrieve/pii/S0735109723063829 |
Accessed | 1/21/2024, 11:35:01 AM |
Volume | 82 |
Pages | 1445-1463 |
Publication | Journal of the American College of Cardiology |
DOI | 10.1016/j.jacc.2023.07.024 |
Issue | 14 |
Journal Abbr | Journal of the American College of Cardiology |
ISSN | 07351097 |
Date Added | 1/21/2024, 11:35:01 AM |
Modified | 1/21/2024, 11:35:36 AM |
Item Type | Journal Article |
---|---|
Author | Toshiaki A Furukawa |
Author | Gordon H Guyatt |
Author | Lauren E Griffith |
Date | 2/2002 |
Language | en |
Short Title | Can we individualize the ‘number needed to treat’? |
Library Catalog | DOI.org (Crossref) |
URL | https://academic.oup.com/ije/article-lookup/doi/10.1093/ije/31.1.72 |
Accessed | 3/3/2024, 3:39:40 PM |
Volume | 31 |
Pages | 72-76 |
Publication | International Journal of Epidemiology |
DOI | 10.1093/ije/31.1.72 |
Issue | 1 |
ISSN | 1464-3685, 0300-5771 |
Date Added | 3/3/2024, 3:39:40 PM |
Modified | 3/3/2024, 3:41:20 PM |
Item Type | Journal Article |
---|---|
Author | Caroline F Finch |
Author | Jill Cook |
Date | 09/2014 |
Language | en |
Short Title | Categorising sports injuries in epidemiological studies |
Library Catalog | DOI.org (Crossref) |
URL | https://bjsm.bmj.com/lookup/doi/10.1136/bjsports-2012-091729 |
Accessed | 12/9/2023, 3:26:13 PM |
Volume | 48 |
Pages | 1276-1280 |
Publication | British Journal of Sports Medicine |
DOI | 10.1136/bjsports-2012-091729 |
Issue | 17 |
Journal Abbr | Br J Sports Med |
ISSN | 0306-3674, 1473-0480 |
Date Added | 12/9/2023, 3:26:13 PM |
Modified | 12/9/2023, 3:27:49 PM |
Item Type | Journal Article |
---|---|
Author | Charlotte Dugourd |
Author | Amna Abichou‐Klich |
Author | René Ecochard |
Author | Fabien Subtil |
Abstract | Classifying patient biomarker trajectories into groups has become frequent in clinical research. Mixed effects classification models can be used to model the heterogeneity of longitudinal data. The estimated parameters of typical trajectories and the partition can be provided by the classification version of the expectation maximization algorithm, named CEM. However, the variance of the parameter estimates obtained underestimates the true variance because classification uncertainties are not taken into account. This article takes into account these uncertainties by using the stochastic EM algorithm (SEM), a stochastic version of the CEM algorithm, after convergence of the CEM algorithm. The simulations showed correct coverage probabilities of the 95% confidence intervals (close to 95% except for scenarios with high bias in typical trajectories). The method was applied on a trial, called low‐cyclo, that compared the effects of low vs standard cyclosporine A doses on creatinine levels after cardiac transplantation. It identified groups of patients for whom low‐dose cyclosporine may be relevant, but with high uncertainty on the dose‐effect estimate. |
Date | 2023-11-10 |
Language | en |
Short Title | Clustering of trajectories with mixed effects classification model |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.9876 |
Accessed | 12/9/2023, 11:38:59 AM |
Volume | 42 |
Pages | 4570-4581 |
Publication | Statistics in Medicine |
DOI | 10.1002/sim.9876 |
Issue | 25 |
Journal Abbr | Statistics in Medicine |
ISSN | 0277-6715, 1097-0258 |
Date Added | 12/9/2023, 11:38:59 AM |
Modified | 12/9/2023, 11:40:33 AM |
Item Type | Journal Article |
---|---|
Author | Suzanne M. Dufault |
Author | Angela M. Crook |
Author | Katie Rolfe |
Author | Patrick P. J. Phillips |
Abstract | We propose a multi‐metric flexible Bayesian framework to support efficient interim decision‐making in multi‐arm multi‐stage phase II clinical trials. Multi‐arm multi‐stage phase II studies increase the efficiency of drug development, but early decisions regarding the futility or desirability of a given arm carry considerable risk since sample sizes are often low and follow‐up periods may be short. Further, since intermediate outcomes based on biomarkers of treatment response are rarely perfect surrogates for the primary outcome and different trial stakeholders may have different levels of risk tolerance, a single hypothesis test is insufficient for comprehensively summarizing the state of the collected evidence. We present a Bayesian framework comprised of multiple metrics based on point estimates, uncertainty, and evidence towards desired thresholds (a Target Product Profile) for (1) ranking of arms and (2) comparison of each arm against an internal control. Using a large public‐private partnership targeting novel TB arms as a motivating example, we find via simulation study that our multi‐metric framework provides sufficient confidence for decision‐making with sample sizes as low as 30 patients per arm, even when intermediate outcomes have only moderate correlation with the primary outcome. Our reframing of trial design and the decision‐making procedure has been well‐received by research partners and is a practical approach to more efficient assessment of novel therapeutics. |
Date | 12/2023 |
Language | en |
Library Catalog | DOI.org (Crossref) |
URL | https://onlinelibrary.wiley.com/doi/10.1002/sim.9961 |
Accessed | 12/9/2023, 11:01:39 AM |
Pages | sim.9961 |
Publication | Statistics in Medicine |
DOI | 10.1002/sim.9961 |
Journal Abbr | Statistics in Medicine |
ISSN | 0277-6715, 1097-0258 |
Date Added | 12/9/2023, 11:01:39 AM |
Modified | 12/9/2023, 11:02:00 AM |
Item Type | Journal Article |
---|---|
Author | Gary S Collins |
Author | Paula Dhiman |
Author | Jie Ma |
Author | Michael M Schlussel |
Author | Lucinda Archer |
Author | Ben Van Calster |
Author | Frank E Harrell |
Author | Glen P Martin |
Author | Karel G M Moons |
Author | Maarten Van Smeden |
Author | Matthew Sperrin |
Author | Garrett S Bullock |
Author | Richard D Riley |
Date | 2024-01-08 |
Language | en |
Short Title | Evaluation of clinical prediction models (part 1) |
Library Catalog | DOI.org (Crossref) |
URL | https://www.bmj.com/lookup/doi/10.1136/bmj-2023-074819 |
Accessed | 1/9/2024, 2:56:11 AM |
Pages | e074819 |
Publication | BMJ |
DOI | 10.1136/bmj-2023-074819 |
Journal Abbr | BMJ |
ISSN | 1756-1833 |
Date Added | 1/9/2024, 2:56:11 AM |
Modified | 1/9/2024, 2:58:18 AM |
Item Type | Journal Article |
---|---|
Author | Zachary Orion Binney |
Author | Mohammad Ali Mansournia |
Date | 2023-11-28 |
Language | en |
Short Title | Methods matter |
Library Catalog | DOI.org (Crossref) |
URL | https://bjsm.bmj.com/lookup/doi/10.1136/bjsports-2023-107599 |
Accessed | 12/9/2023, 4:18:53 PM |
Pages | bjsports-2023-107599 |
Publication | British Journal of Sports Medicine |
DOI | 10.1136/bjsports-2023-107599 |
Journal Abbr | Br J Sports Med |
ISSN | 0306-3674, 1473-0480 |
Date Added | 12/9/2023, 4:18:53 PM |
Modified | 12/9/2023, 4:21:08 PM |
Item Type | Journal Article |
---|---|
Author | P. M. Aronow |
Author | James M. Robins |
Author | Theo Saarinen |
Author | Fredrik Sävje |
Author | Jasjeet Sekhon |
Abstract | We argue that randomized controlled trials (RCTs) are special even among settings where average treatment effects are identified by a nonparametric unconfoundedness assumption. This claim follows from two results of Robins and Ritov (1997): (1) with at least one continuous covariate control, no estimator of the average treatment effect exists which is uniformly consistent without further assumptions, (2) knowledge of the propensity score yields a uniformly consistent estimator and honest confidence intervals that shrink at parametric rates with increasing sample size, regardless of how complicated the propensity score function is. We emphasize the latter point, and note that successfully-conducted RCTs provide knowledge of the propensity score to the researcher. We discuss modern developments in covariate adjustment for RCTs, noting that statistical models and machine learning methods can be used to improve efficiency while preserving finite sample unbiasedness. We conclude that statistical inference has the potential to be fundamentally more difficult in observational settings than it is in RCTs, even when all confounders are measured. |
Date | 2021 |
Library Catalog | DOI.org (Datacite) |
URL | https://arxiv.org/abs/2108.11342 |
Accessed | 3/3/2024, 3:48:09 PM |
Rights | arXiv.org perpetual, non-exclusive license |
Extra | Publisher: [object Object] Version Number: 2 |
DOI | 10.48550/ARXIV.2108.11342 |
Date Added | 3/3/2024, 3:48:09 PM |
Modified | 3/3/2024, 3:50:17 PM |