Model-Robust Inference for Clinical Trials That Improve Precision by Stratified Randomization and Covariate Adjustment

• Activity Number: 166 - Non-Clinical Statistics, Personalized Medicine, and Other Topics
• Type: Contributed
• Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Bingkai Wang* and Ryoko Susukida and Ramin Mojtabai and Masoumeh Amin-Esmaeili and Michael Rosenblum
• Companies: Johns Hopkins Bloomberg School of Public Health and Department of Mental Health, Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Department of Mental Health, Johns Hopkins Bloomberg School of Public Health and Johns Hopkins University
• Keywords: Covariate-adaptive randomization; generalized linear model; robustness

Abstract

“ (*) Student Paper Award Winner” Two commonly used methods for improving precision and power in clinical trials are stratified randomization and covariate adjustment. However, many trials do not fully capitalize on the combined precision gains from these two methods. We derive consistency and asymptotic normality of model-robust estimators that combine these two methods, and show that these estimators can lead to substantial gains in precision and power. Our theorems cover a class of estimators that handle continuous, binary, and time-to-event outcomes; missing outcomes under the missing at random assumption are handled as well. For each estimator, we give a formula for a consistent variance estimator that is model-robust and that fully captures variance reductions from stratified randomization and covariate adjustment. Also, we give the first proof of consistency and asymptotic normality of the Kaplan-Meier estimator under stratified randomization, and we derive its asymptotic variance. The above results also hold for the biased-coin covariate-adaptive design. We demonstrate our results using data from two trials of substance use disorder treatments.