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Bayesian Models for Multivariate Current Status Data with Informative Censoring
Author(s) -
Dunson David B.,
Dinse Gregg E.
Publication year - 2002
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2002.00079.x
Subject(s) - censoring (clinical trials) , covariate , statistics , multivariate statistics , markov chain monte carlo , bayesian probability , event (particle physics) , econometrics , nonparametric statistics , conditional probability distribution , computer science , joint probability distribution , event data , mathematics , quantum mechanics , physics
Summary. 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.