Fixed effects, random effects and GEE: what are the differences?
For analyses of longitudinal repeated-measures data, statistical methods include the random effects model, fixed effects model and the method of generalized estimating equations. We examine the assumptions that underlie these approaches to assessing covariate effects on the mean of a continuous, dichotomous or count outcome. Access to statistical software to implement these models has led to widespread application in numerous disciplines. However, careful consideration should be paid to their critical assumptions to ascertain which model might be appropriate in a given setting. To illustrate similarities and differences that might exist in empirical results, we use a study that assessed depressive symptoms in low-income pregnant women using a structured instrument with up to five assessments that spanned the pre-natal and post-natal periods. Understanding the conceptual differences between the methods is important in their proper application even though empirically they might not differ substantively. The choice of model in specific applications would depend on the relevant questions being addressed, which in turn informs the type of design and data collection that would be relevant.
Gardiner
Joseph C
JC
Division of Biostatistics, Department of Epidemiology, Michigan State University, East Lansing, MI 48824, USA. jgardiner@epi.msu.edu
Luo
Zhehui
Z
Roman
Lee Anne
LA
D003150:Community Health Workers; D003863:Depression / Q000503:physiopathology; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D009726:Nurses; D010348:Patient Care Team / Q000706:statistics & numerical data; D011897:Random Allocation