Mixed-effects Models for Repeated Measures in Longitudinal Data Analysis: An Introduction to Methodology, Theory, and Applications

Abstract

The presence of missing data has seriously compromised statistical inferences from clinical trials. Mixed-effects models for repeated measures (MMRM) provide a useful approach for analyzing incomplete longitudinal data. In particular, MMRM analysis is increasingly common in biomedical research and is frequently used as the primary analysis in these trials. We introduce the basic ideas, model structure, and properties of MMRM. In addition, we give an overview of parameter estimates and statistical inference for MMRM analysis. Finally, we discuss some important considerations regarding MMRM analysis.