title: The use of GEE for analyzing longitudinal binomial data: a primer using data from a tobacco intervention.
abstract: Longitudinal study designs in addictive behaviors research are common as researchers have focused increasingly on how various explanatory variables affect responses over time. In particular, such designs are used in intervention studies that have multiple follow-up points. These designs typically involve repeated measurement of participants' responses, and thus correlation within each participant is expected. Correct inferences can only be obtained by taking into account this within-participant correlation between repeated measurements, which can complicate the analysis of longitudinal data. In recent years, generalized estimating equations (GEE) has become a standard method for analyzing non-normal longitudinal data, yet it often is not utilized by addiction researchers. The goal of this article is to provide an overview of the GEE approach for analyzing correlated binary data for behavioral researchers, using data from an intervention study on the prevention of relapse to tobacco smoking.
authors.affiliation: H. Lee Moffitt Cancer Center and Research Institute, The University of South Florida, Tampa, Florida 33612, USA. leej@moffitt.usf.edu
authors.forename: Ji-Hyun
authors.initials: JH
authors.lastname: Lee
authors.forename: Thaddeus A
authors.initials: TA
authors.lastname: Herzog
authors.forename: Cathy D
authors.initials: CD
authors.lastname: Meade
authors.forename: Monica S
authors.initials: MS
authors.lastname: Webb
authors.forename: Thomas H
authors.initials: TH
authors.lastname: Brandon
mesh_terms: D000328:Adult; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D016032:Randomized Controlled Trials as Topic; D016540:Smoking Cessation / Q000379:methods / Q000706:statistics & numerical data*; D012984:Software