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

TL;DR

This paper introduces IMMRM, an extension of the mixed model for repeated measures, which enhances robustness and precision in analyzing randomized trial data, especially under model misspecification and missing data conditions.

Contribution

The paper proposes IMMRM, a robust extension of MMRM, that improves treatment effect estimation accuracy and precision in randomized trials with repeated measures.

Findings

IMMRM is robust to model misspecification under certain conditions.

IMMRM achieves equal or greater precision than traditional methods.

Simulation and real data analyses support IMMRM's advantages.

Abstract

In randomized 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 primary endpoint. MMRM, however, can suffer from bias and precision loss when it models intermediate outcomes incorrectly, and hence fails to use the post-randomization information harmlessly. This paper proposes an extension of the commonly used MMRM, called IMMRM, that improves the robustness and optimizes the precision gain from covariate adjustment, stratified randomization, and adjustment for intermediate outcome measures. Under regularity conditions and missing completely at random, we prove that the IMMRM estimator for the average treatment effect is robust to arbitrary model misspecification and is asymptotically equal or more precise than the analysis of covariance (ANCOVA) estimator and the MMRM estimator. Under missing at random, IMMRM is less likely to be misspecified than MMRM, and we demonstrate via simulation studies that IMMRM continues to have less bias and smaller variance. Our results are further supported by a re-analysis of a randomized trial for the treatment of diabetes.