title: Multilevel logistic regression modelling with correlated random effects: application to the Smoking Cessation for Youth study.
abstract: A multilevel logistic regression model is presented for the analysis of clustered and repeated binary response data. At the subject level, serial dependence is expected between repeated measures recorded on the same individual. At the cluster level, correlations of observations within the same subgroup are present due to the inherent hierarchical setting. Two random components are therefore incorporated explicitly within the linear predictor to account for the simultaneous heterogeneity and autoregressive structure. Application to analyse a set of longitudinal data from an adolescent smoking cessation intervention that motivated this study is illustrated.
authors.affiliation: School of Public Health, Curtin University of Technology, Perth, Australia. K.Wang@exchange.curtin.edu.au
authors.forename: Kui
authors.initials: K
authors.lastname: Wang
authors.forename: Andy H
authors.initials: AH
authors.lastname: Lee
authors.forename: Greg
authors.initials: G
authors.lastname: Hamilton
authors.forename: Kelvin K W
authors.initials: KK
authors.lastname: Yau
mesh_terms: D000293:Adolescent; D002648:Child; D016000:Cluster Analysis; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D016540:Smoking Cessation / Q000379:methods*; D000074606:Smoking Prevention; D012959:Socioeconomic Factors; D014904:Western Australia