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

Mixed models for repeated measures should include time-by-covariate  interactions to assure power gains and robustness against dropout bias  relative to complete-case ANCOVA