Table 5.

Comparisons of the different aspects of the generalized estimating equation model and the mixed effects model for repeated measures

Regression ModelsGEEMixed Effects
Model components and parametersMean response model and error termFixed and random effects and error term
Non-normal outcomeGEE with specified link functionsGLMM with specified link functions
UsageAssociation/predict population-average trajectoryAssociation/predict both population-average and individual trajectories
Goodness of fit metricsQuasilikelihood information criterionAikake Information Criterion/Bayesian Information Criterion
Correlation structurePrespecified working correlation (e.g., independence, exchangeable, autoregressive, m dependent, unstructured)Correlation structure induced by both random effects and error term; more flexible in partitioning variability among various hierarchies
Missing assumptionsCovariate-dependent MCAR; cannot handle missing not at random or informative censoringMCAR and missing at random; cannot handle missing not at random or informative censoring
Pros and consRobust for misspecification of correlation structuresSuitable for data with high subject heterogeneity; higher computational cost
  • Repeated measures as outcome. GEE, generalized estimating equation; GLMM, generalized linear mixed model; MCAR, missing completely at random.