Table 5.

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

Regression Models	GEE	Mixed Effects
Model components and parameters	Mean response model and error term	Fixed and random effects and error term
Non-normal outcome	GEE with specified link functions	GLMM with specified link functions
Usage	Association/predict population-average trajectory	Association/predict both population-average and individual trajectories
Goodness of fit metrics	Quasilikelihood information criterion	Aikake Information Criterion/Bayesian Information Criterion
Correlation structure	Prespecified 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 assumptions	Covariate-dependent MCAR; cannot handle missing not at random or informative censoring	MCAR and missing at random; cannot handle missing not at random or informative censoring
Pros and cons	Robust for misspecification of correlation structures	Suitable 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.