Bayesian models for multivariate current status data with informative censoring.

Abstract:

:Multivariate current status data, consist of indicators of whether each of several events occur by the time of a single examination. Our interest focuses on inferences about the joint distribution of the event times. Conventional methods for analysis of multiple event-time data cannot be used because all of the event times are censored and censoring may be informative. Within a given subject, we account for correlated event times through a subject-specific latent variable, conditional upon which the various events are assumed to occur independently. We also assume that each event contributes independently to the hazard of censoring. Nonparametric step functions are used to characterize the baseline distributions of the different event times and of the examination times. Covariate and subject-specific effects are incorporated through generalized linear models. A Markov chain Monte Carlo algorithm is described for estimation of the posterior distributions of the unknowns. The methods are illustrated through application to multiple tumor site data from an animal carcinogenicity study.

journal_name

Biometrics

journal_title

Biometrics

authors

Dunson DB,Dinse GE

doi

10.1111/j.0006-341x.2002.00079.x

subject

Has Abstract

pub_date

2002-03-01 00:00:00

pages

79-88

issue

1

eissn

0006-341X

issn

1541-0420

journal_volume

58

pub_type

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