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
Biometricsjournal_title
Biometricsauthors
Dunson DB,Dinse GEdoi
10.1111/j.0006-341x.2002.00079.xsubject
Has Abstractpub_date
2002-03-01 00:00:00pages
79-88issue
1eissn
0006-341Xissn
1541-0420journal_volume
58pub_type
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