Robustly leveraging the post-randomization information to improve precision in the analyses of randomized clinical trials
In randomized clinical trials, repeated measures of the outcome are routinely collected. The mixed model for repeated measures (MMRM) leverages the information from these repeated outcome measures, and is often used for the primary analysis to estimate the average treatment effect at the final visit. MMRM, however, can suffer from precision loss when it models the intermediate outcomes incorrectly, and hence fails to use the post-randomization information in a harmless way. In this paper, we propose a new working model, called IMMRM, that generalizes MMRM and optimizes the precision gain from covariate adjustment, stratified randomization and adjustment for intermediate outcome measures. We prove that the IMMRM estimator for the average treatment effect is consistent and asymptotically normal under arbitrary misspecification of its working model assuming missing completely at random. Under simple or stratified randomization, the IMMRM estimator is asymptotically equally or more precise than the analysis of covariance (ANCOVA) estimator and the MMRM estimator. By re-analyzing three randomized trials in the diabetes therapeutic area, we demonstrate that the IMMRM estimator has 2-24% smaller variance than ANCOVA and 5-16% smaller variance than MMRM.