Model-Robust Inference for Clinical Trials That Improve Precision by Stratified Randomization and Adjustment for Additional Baseline Variables

• Activity Number: 63 - Power and Sample Size: Methods and Applications
• Type: Contributed
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Bingkai Wang* and Michael Rosenblum
• Companies: Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Univ, Bloomberg School of Public Health
• Keywords: Covariate-adaptive randomization; generalized linear model; robustness

Abstract

We focus on estimating the average treatment effect in clinical trials that involve stratified randomization, which is commonly used. It is important to understand the large sample properties of estimators that adjust for stratum variables and additional baseline variables, since this can lead to substantial gains in precision and power. Surprisingly, to the best of our knowledge, this is an open problem. We generalize the results by by Bugni et al. (2018) in three directions. First, in addition to continuous outcomes, we handle binary and time-to-event outcomes. Second, we allow adjustment for an additional, preplanned set of baseline variables, which can improve precision. Third, we handle missing outcomes under the missing at random assumption. We prove that a wide class of estimators is asymptotically normally distributed under stratified randomization and has equal or smaller asymptotic variance than under simple randomization. For each estimator in this class, we give a consistent variance estimator. The above results also hold for the biased-coin covariate-adaptive design. We demonstrate our results using completed trial data sets of treatments for substance use disorder.