An Overview of Current Software Procedures for Fitting Linear Mixed Models

• Activity Number: 464
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Computing
• Author(s): Brady West*+ and Andrzej Galecki
• Companies: University of Michigan and University of Michigan
• Keywords: Linear Mixed Models ; Multilevel Models ; Models for Clustered Data ; Longitudinal Data Analysis ; Covariance Structures ; Statistical Software

Abstract

At present, there are many software procedures available enabling statisticians to fit linear mixed models (LMMs) to continuous dependent variables in clustered or longitudinal data sets. LMMs are flexible tools for analyzing relationships among variables in these types of data sets, in that a variety of covariance structures can be used depending on the subject matter under study. While 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. We sought 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. This presentation will summarize our findings and provide recommendations for statisticians using LMMs in practice.