An Overview of Current Software Procedures for Fitting Linear Mixed Models

At present, there are many software procedures available that enable statisticians to fit linear mixed models (LMMs) to continuous dependent variables in clustered or longitudinal datasets. LMMs are flexible tools for analyzing relationships among variables in these types of datasets, in that a variety of covariance structures can be used depending on the subject matter under study. The explicit random effects in LMMs allow analysts to make inferences about the variability between clusters or subjects in larger hypothetical populations, and examine cluster- or subject-level variables that explain portions of this variability. These models can also be used to analyze longitudinal or clustered datasets with data that are missing at random (MAR), and can accommodate time-varying covariates in longitudinal datasets. Although the software procedures currently available have many features in common, more specific analytic aspects of fitting LMMs (e.g., crossed random effects, appropriate hypothesis testing for variance components, diagnostics, incorporating sampling weights) may only be available in selected software procedures. With this article, we aim to perform a comprehensive and up-to-date comparison of the current capabilities of software procedures for fitting LMMs, and provide statisticians with a guide for selecting a software procedure appropriate for their analytic goals.