A Bayesian approach for joint modeling of cluster size and subunit-specific outcomes.
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.
Dunson
David B
DB
Biostatistics Branch, MD A3-03, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, North Carolina 27709, USA. dunson1@niehs.nih.gov
Chen
Zhen
Z
Harry
Jean
J
D000818:Animals; D001499:Bayes Theorem; D001699:Biometry; D016000:Cluster Analysis; D005314:Embryonic and Fetal Development / Q000187:drug effects; D019855:Ethylene Glycol / Q000633:toxicity; D005260:Female; D051379:Mice; D008954:Models, Biological; D015233:Models, Statistical; D011247:Pregnancy
D019855:Ethylene Glycol