Multilevel logistic regression modelling with correlated random effects: Application to the Smoking Cessation for Youth study

Wang, Kui, Lee, Andy H., Hamilton, Greg and Yau, Kelvin K.W. (2006) Multilevel logistic regression modelling with correlated random effects: Application to the Smoking Cessation for Youth study. Statistics in Medicine, 25 22: 3864-3876. doi:10.1002/sim.2472


Author Wang, Kui
Lee, Andy H.
Hamilton, Greg
Yau, Kelvin K.W.
Title Multilevel logistic regression modelling with correlated random effects: Application to the Smoking Cessation for Youth study
Journal name Statistics in Medicine   Check publisher's open access policy
ISSN 0277-6715
1097-0258
Publication date 2006-11-30
Sub-type Article (original research)
DOI 10.1002/sim.2472
Open Access Status
Volume 25
Issue 22
Start page 3864
End page 3876
Total pages 13
Place of publication Chichester, England
Publisher Wiley
Language eng
Subject 0104 Statistics
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.
Keyword Non-parametric maximum likelihood (NPML)
Random effects
Repeated binary data
School-based intervention
Serial correlation
Smoking cessation
Q-Index Code C1
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
School of Mathematics and Physics
 
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