Bayesian latent variable models for mixed discrete outcomes.

Journal Article (Journal Article)

In studies of complex health conditions, mixtures of discrete outcomes (event time, count, binary, ordered categorical) are commonly collected. For example, studies of skin tumorigenesis record latency time prior to the first tumor, increases in the number of tumors at each week, and the occurrence of internal tumors at the time of death. Motivated by this application, we propose a general underlying Poisson variable framework for mixed discrete outcomes, accommodating dependency through an additive gamma frailty model for the Poisson means. The model has log-linear, complementary log-log, and proportional hazards forms for count, binary and discrete event time outcomes, respectively. Simple closed form expressions can be derived for the marginal expectations, variances, and correlations. Following a Bayesian approach to inference, conditionally-conjugate prior distributions are chosen that facilitate posterior computation via an MCMC algorithm. The methods are illustrated using data from a Tg.AC mouse bioassay study.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB; Herring, AH

Published Date

  • January 2005

Published In

Volume / Issue

  • 6 / 1

Start / End Page

  • 11 - 25

PubMed ID

  • 15618524

Electronic International Standard Serial Number (EISSN)

  • 1468-4357

International Standard Serial Number (ISSN)

  • 1465-4644

Digital Object Identifier (DOI)

  • 10.1093/biostatistics/kxh025


  • eng