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A Bayesian Approach for Joint Modeling of Cluster Size and Subunit‐Specific Outcomes
Author(s) -
Dunson David B.,
Chen Zhen,
Harry Jean
Publication year - 2003
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/1541-0420.00062
Subject(s) - categorical variable , bayesian probability , computer science , gibbs sampling , cluster (spacecraft) , dependency (uml) , latent variable , probit , posterior probability , bayesian inference , econometrics , statistics , data mining , mathematics , machine learning , artificial intelligence , programming language
Summary . In applications that involve clustered data, such as longitudinal studies and developmental toxicity experiments, the number of subunits within a cluster is often correlated with outcomes measured on the individual subunits. Analyses that ignore this dependency can produce biased inferences. This article proposes a Bayesian framework for jointly modeling cluster size and multiple categorical and continuous outcomes measured on each subunit. We use a continuation ratio probit model for the cluster size and underlying normal regression models for each of the subunit‐specific outcomes. Dependency between cluster size and the different outcomes is accommodated through a latent variable structure. The form of the model facilitates posterior computation via a simple and computationally efficient Gibbs sampler. The approach is illustrated with an application to developmental toxicity data, and other applications, to joint modeling of longitudinal and event time data, are discussed.