Joint Models for the Association of Longitudinal Binary and Continuous Processes With Application to a Smoking Cessation Trial

• 成果类型: Article
• 署名作者: Liu, Xuefeng; Daniels, Michael J.; Marcus, Bess
• 署名单位: East Tennessee State University; State University System of Florida; University of Florida; Brown University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214508000000904
• 发表日期: 2009
• 页码: 429-438
• 关键词: latent variable models; base-line data; parameter expansion; posterior distributions; Covariance matrices; clustered binary; selection; discrete; efficacy; regression

摘要

Joint models for the association of a logitudinal binary and a longitudinal continuous process are proposed for situations in which their association is of direct interest. The models are parametrized such that the dependence between the two processes is characterized by unconstrained regression coefficients. Bayesian variable selection techniques are used to parsimoniously model these coefficients. A Markov chain Monte Carlo (MCMC) sampling algorithm is developed for sampling from the posterior distribution, using data augmentation steps to handle missing data. Several technical issues are addressed to implement the MCMC algorithm efficiently. The models are motivated by, and are used for, the analysis of a smoking cessation clinical trial in which an important question of interest was the effect of the (exercise) treatment on the relationship between smoking cessation and weight gain.