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.
翻译:在随机临床试验中,通常会收集重复结果的反复计量,重复措施的混合模式(MMRM)利用这些重复结果措施的信息,并经常用于初级分析,以估计最终访问的平均治疗效果。然而,如果MMRM错误地模拟中间结果,从而不能以无害的方式使用自发后的信息,它可能会遭受精确损失。在本文中,我们提出了一个称为IMMRM的新的工作模式,该模式将MMRM概括化,并优化从共变调整、分批随机调整和中期结果措施调整中获得的精准收益。我们证明,IMMR平均治疗效果的估算器在任意错误地错误地设定其工作模式时,假定完全随机失踪,因此可能会遭受精确损失。在简单或分解的随机化下,IMMR估计器的估算器与MARM(AN)估计器和MRMR(M)的测算器相比,比RMM(IM)-24)的最小的三次随机化试验显示,比RMM(RM)和RM(RM)的5-24)变压面积小于RMA的2和A(RMMA)的两次更小的变压区域显示,显示我们的RMMM(RM)和RM)的5-24)和RMA(RMA(RM)-24)的变压的变压2的变压的比。