Bayesian incidence analysis of animal tumorigenicity data

Published

Journal Article

Statistical inference about tumorigenesis should focus on the tumour incidence rate. Unfortunately, in most animal carcinogenicity experiments, tumours are not observable in live animals and censoring of the tumour onset times is informative. In this paper, we propose a Bayesian method for analysing data from such studies. Our approach focuses on the incidence of tumours and accommodates occult tumours and censored onset times without restricting tumour lethality, relying on cause-of-death data, or requiring interim sacrifices. We represent the underlying state of nature by a multistate stochastic process and assume general probit models for the time-specific transition rates. These models allow the incorporation of covariates, historical control data and subjective prior information. The inherent flexibility of this approach facilitates the interpretation of results, particularly when the sample size is small or the data are sparse. We use a Gibbs sampler to estimate the relevant posterior distributions. The methods proposed are applied to data from a US National Toxicology Program carcinogenicity study.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB; Dinse, GE

Published Date

  • January 1, 2001

Published In

Volume / Issue

  • 50 / 2

Start / End Page

  • 125 - 141

International Standard Serial Number (ISSN)

  • 0035-9254

Digital Object Identifier (DOI)

  • 10.1111/1467-9876.00224

Citation Source

  • Scopus