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In randomized trials, repeated measures of the outcome are routinelycollected. The mixed model for repeated measures (MMRM) leverages theinformation from these repeated outcome measures, and is often used for theprimary analysis to estimate the average treatment effect at the primaryendpoint. MMRM, however, can suffer from bias and precision loss when it modelsintermediate outcomes incorrectly, and hence fails to use thepost-randomization information harmlessly. This paper proposes an extension ofthe commonly used MMRM, called IMMRM, that improves the robustness andoptimizes the precision gain from covariate adjustment, stratifiedrandomization, and adjustment for intermediate outcome measures. Underregularity conditions and missing completely at random, we prove that the IMMRMestimator for the average treatment effect is robust to arbitrary modelmisspecification and is asymptotically equal or more precise than the analysisof covariance (ANCOVA) estimator and the MMRM estimator. Under missing atrandom, IMMRM is less likely to be misspecified than MMRM, and we demonstratevia simulation studies that IMMRM continues to have less bias and smallervariance. Our results are further supported by a re-analysis of a randomizedtrial for the treatment of diabetes.

Improving the mixed model for repeated measures to robustly increase precision in randomized trials