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.

翻译：在随机试验中，结局指标的重复测量通常被收集。重复测量混合模型（MMRM）利用这些重复结局测量的信息，常被用于主分析，以估计主要终点的平均处理效应。然而，当MMRM对中间结局的建模不正确时，它可能产生偏倚和精度损失，从而无法无害地利用随机后信息。本文提出了一种常用MMRM的扩展形式，称为IMMRM，它提高了稳健性，并优化了协变量调整、分层随机化及中间结局测量调整带来的精度提升。在正则条件和完全随机缺失假设下，我们证明IMMRM对平均处理效应的估计量对任意模型误设具有稳健性，且渐近等于或优于协方差分析（ANCOVA）估计量和MMRM估计量的精度。在随机缺失假设下，IMMRM比MMRM更不易误设，并通过模拟研究证明IMMRM持续具有更小的偏倚和方差。我们的结果进一步得到一项糖尿病治疗随机试验的重分析支持。