Article
Statistics & Probability
Xiudi Li, Sijia Li, Alex Luedtke
Summary: We introduce a framework to utilize external data for assessing the efficiency of covariate-adjusted estimators compared to unadjusted estimators in future randomized trials. The relative efficiencies obtained approximate the required sample size ratio for desired statistical power. We develop semiparametrically efficient estimators for various treatment effect estimands of interest, allowing for flexible statistical learning methods to estimate the nuisance functions. We propose a Wald-type confidence interval and a double bootstrap scheme for statistical inference. Simulation studies demonstrate the performance of the proposed methods, and they are applied to estimate the efficiency gain of covariate adjustment in Covid-19 therapeutic trials.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2023)
Article
Nutrition & Dietetics
Christian Ritz
Summary: This tutorial provides a detailed introduction to the statistical analysis of parallel-arm RCTs in nutrition, focusing on how trial design and other factors may influence subsequent statistical analysis. It covers all steps of the statistical analysis and includes a practical example.
EUROPEAN JOURNAL OF CLINICAL NUTRITION
(2021)
Article
Statistics & Probability
Ting Ye, Jun Shao, Yanyao Yi, Qingyuan Zhao
Summary: This article presents best practices for covariate adjustment in simple or covariate-adaptive randomized trials using a model-assisted approach. The recommendations include guaranteed efficiency gain, wide applicability, and robust standard error estimation.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2022)
Article
Mathematical & Computational Biology
Satoshi Hattori, Sho Komukai, Tim Friede
Summary: In randomized clinical trials, incorporating baseline covariates can improve the power in hypothesis testing for treatment effects. The Cox proportional hazards model with baseline covariates as explanatory variables can improve the standard logrank test. We propose a simple strategy for sizing randomized clinical trials utilizing historical data and derive a power formula for the augmented logrank test.
STATISTICS IN MEDICINE
(2022)
Review
Cardiac & Cardiovascular Systems
Leah Pirondini, John Gregson, Ruth Owen, Tim Collier, Stuart Pocock
Summary: This article reviews the current practice of covariate adjustment in cardiovascular trials published in major medical journals in 2019. The study finds that contemporary cardiovascular trials do not make best use of covariate adjustment, and that more frequent use could lead to improvements in the efficiency of future trials.
JACC-HEART FAILURE
(2022)
Article
Statistics & Probability
Leonard Henckel, Emilija Perkovic, Marloes H. Maathuis
Summary: This article introduces a graphical method for covariate adjustment to estimate the total causal effect. By comparing the asymptotic variances provided by different valid adjustment sets, a simple variance decreasing pruning procedure and a graphical characterization of the optimal asymptotic variance are proposed. These results are applicable to various graphical structures, not only causal linear models.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2022)
Article
Clinical Neurology
Dale J. Langford, Sonia Sharma, Michael P. Mcdermott, Avinash Beeram, Soroush Besherat, Fallon O. France, Remington Mark, Meghan Park, Mahd Nishtar, Dennis C. Turk, Robert H. Dworkin, Jennifer S. Gewandter
Summary: The statistical analysis of baseline factors in chronic pain RCTs is inconsistent, and prespecified adjustments for baseline covariates can improve accuracy and assay sensitivity.
Review
Health Care Sciences & Services
Pascale Nevins, Kendra Davis-Plourde, Jules Antoine Pereira Macedo, Yongdong Ouyang, Mary Ryan, Guangyu Tong, Xueqi Wang, Can Meng, Luis Ortiz-Reyes, Fan Li, Agnes Caille, Monica Taljaard
Summary: This study investigates the methods of randomization and reporting of balance at baseline in stepped-wedge cluster randomized trials (SW-CRTs). The findings suggest that most trials use unrestricted allocation for cluster randomization and there are limitations in reporting balance at baseline. The authors recommend that researchers need more guidance on methods of randomization and assessment of baseline balance.
JOURNAL OF CLINICAL EPIDEMIOLOGY
(2023)
Article
Social Sciences, Mathematical Methods
Nicholas Williams, Michael Rosenblum, Ivan Diaz
Summary: This article discusses how to improve statistical estimation accuracy and reduce the number of participants needed in clinical trials through covariate adjustment, focusing on time-to-event and ordinal outcomes. In COVID-19 trials, the l1$$ {\ell}_1 $$-regularization method controls type 1 errors and improves estimation accuracy, even at small sample sizes.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY
(2022)
Article
Clinical Neurology
Dale J. Langford, Raissa Lou, Soun Sheen, Dagmar Amtmann, Luana Colloca, Robert R. Edwards, John T. Farrar, Nathaniel P. Katz, Michael P. McDermott, Bryce B. Reeve, Ajay D. Wasan, Dennis C. Turk, Robert H. Dworkin, Jennifer S. Gewandter
Summary: Variability in pain outcomes can hinder the sensitivity of chronic pain clinical trials. Participants' expectations may contribute to this variability and impede the development of new pain treatments. Measurement and management of expectations in clinical trials need to be optimized and standardized. This article provides an overview of research findings on the relationship between baseline expectations and pain outcomes, emphasizing the potential benefit of adjusting for participants' expectations in trial analyses.
Article
Biology
David Benkeser, Ivan Diaz, Alex Luedtke, Jodi Segal, Daniel Scharfstein, Michael Rosenblum
Summary: Time is crucial in assessing potential drugs for COVID-19. Despite recommendations from regulatory agencies, covariate adjustment is underutilized in trials. Simulation studies show that using covariate adjustment can significantly reduce sample size and improve efficiency in COVID-19 trials.
Article
Mathematical & Computational Biology
Sandra Siegfried, Stephen Senn, Torsten Hothorn
Summary: The question of how to leverage individual patient data for designing more powerful clinical trials becomes increasingly important in the era of digitalization. Incorporating historical information in the design and analysis of future clinical trials can lead to smaller yet equally powerful studies. A study found that adjusting the analysis with respect to a prognostic score obtained from historical data can significantly reduce the required sample size.
BIOMETRICAL JOURNAL
(2023)
Article
Mathematical & Computational Biology
Lisa N. Yelland, Jennie Louise, Brennan C. Kahan, Tim P. Morris, Katherine J. Lee, Thomas R. Sullivan
Summary: Many trials use stratified randomisation to allocate participants, but it is unclear how to adjust for stratification variables affected by misclassification. A simulation study comparing different adjustment methods for continuous outcomes was conducted. Adjusting for the true strata was found to be optimal, while adjusting for the randomisation strata or the updated strata depended on the specific setting. The updated strata method is recommended for adjustment, along with subgroup analyses, in order to address stratification errors in practice.
STATISTICS IN MEDICINE
(2023)
Editorial Material
Biology
Michael A. Proschan
Summary: The study evaluates the efficiency gains of covariate adjustment in settings with different outcomes and shows its benefits in quickly obtaining answers. The suggested approach is an important weapon in fighting against epidemics.
Article
Medicine, Research & Experimental
Raphaelle Momal, Honghao Li, Paul Trichelair, Michael G. B. Blum, Felix Balazard
Summary: Adjustment for prognostic covariates increases the statistical power of randomized trials. Factors influencing power and sample size requirements in time-to-event trials were studied using both parametric simulations and simulations derived from the Cancer Genome Atlas cohort of hepatocellular carcinoma patients. Simulations demonstrated that the benefit of covariate adjustment increases with the prognostic performance of the adjustment covariate and with the cumulative incidence of the event in the trial. Broadening eligibility criteria can be maintained with adequate covariate adjustment and the Cox-Snell R-CS(2) is a conservative estimation of the reduction in sample size requirements provided by covariate adjustment. Code and results are available at https://github.com/owkin/CovadjustSim.