A Bayesian approach for joint modeling of cluster size and subunit-specific outcomes.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB; Chen, Z; Harry, J

Published Date

  • September 2003

Published In

Volume / Issue

  • 59 / 3

Start / End Page

  • 521 - 530

PubMed ID

  • 14601753

Pubmed Central ID

  • 14601753

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

International Standard Serial Number (ISSN)

  • 0006-341X

Digital Object Identifier (DOI)

  • 10.1111/1541-0420.00062

Language

  • eng