In randomized trials with continuous-valued outcomes the goal is often to estimate the difference in average outcomes between two treatment groups. However, the outcome in some trials is longitudinal, meaning that multiple measurements of the same outcome are taken over time for each subject. The target of inference in this case is often still the difference in averages at a given timepoint. One way to analyze these data is to ignore the measurements at intermediate timepoints and proceed with a standard covariate-adjusted analysis (e.g. ANCOVA) with the complete cases. However, it is generally thought that exploiting information from intermediate timepoints using mixed models for repeated measures (MMRM) a) increases power and b) more naturally "handles" missing data. Here we prove that neither of these conclusions is entirely correct when baseline covariates are adjusted for without including time-by-covariate interactions. We back these claims up with simulations. MMRM provides benefits over complete-cases ANCOVA in many cases, but covariate-time interaction terms should always be included to guarantee the best results.

翻译：在连续评估结果的随机试验中,目标往往是估计两个治疗组之间平均结果的差别,然而,有些试验的结果是纵向的,意味着对同一结果的多重测量是随时间推移而来的。本案的推断目标往往仍然是某一时间点的平均差别。分析这些数据的方法之一是忽略中间时间点的测量结果,对全部案例进行标准的共变调整分析(如ANCOVA)。然而,一般认为利用中间时间点的信息,使用混合模式来重复测量(MMRM) a),增加功率和b)更自然地“手动”缺失的数据。我们在这里证明,当基线共变换调整时没有包括逐个时间的相互作用时,这些结论都没有完全正确。我们用模拟来支持这些主张。MURM为许多案例提供了全例ANCOVA的效益,但共变时间互动术语应该始终包括来保证最佳结果。