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.

中文翻译：

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改进重复测量的混合模型，以大幅提高随机试验的精度

在随机试验中，通常会收集结果的重复测量结果。重复测量混合模型 (MMRM) 利用这些重复结果测量的信息，通常用于主要分析，以估计主要终点的平均治疗效果。然而，当 MMRM 对中间结果进行错误建模时，可能会出现偏差和精度损失，因此无法无害地使用随机化后的信息。本文提出了常用 MMRM 的扩展，称为 IMMRM，它提高了鲁棒性并优化了协变量调整、分层随机化和中间结果测量调整的精度增益。在规律性条件下且完全随机缺失的情况下，我们证明平均治疗效果的 IMMRM 估计量对于任意模型错误指定具有鲁棒性，并且渐近等于或更精确于协方差分析 (ANCOVA) 估计量和 MMRM 估计量。在随机缺失的情况下，IMRMM 比 MMRM 更不可能被错误指定，并且我们通过模拟研究证明 IMMRM 仍然具有较小的偏差和较小的方差。对糖尿病治疗随机试验的重新分析进一步支持了我们的结果。