Following my recent post on fitting an MMRM in SAS, R, and Stata, someone recently asked me about when it is preferable to use a Mixed Model Repeated Measures (MMRM) analysis as opposed to a a linear mixed effects model (LME) which includes subject level random effects (e.g. intercepts).
linear mixed models
Mixed model repeated measures (MMRM) in Stata, SAS and R
Linear mixed models are a popular modelling approach for longitudinal or repeated measures data. They extend standard linear regression models through the introduction of random effects and/or correlated residual errors. In the context of randomised trials which repeatedly measure patients over time, linear mixed models are a popular approach of analysis, not least because they handle missing data in the outcome ‘automatically’, under the missing at random assumption. Because of this a mixed model analysis has in many cases become the default method of analysis in clinical trials with a repeatedly measured outcome.