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December 2010 Bayesian semiparametric inference for multivariate doubly-interval-censored data
Alejandro Jara, Emmanuel Lesaffre, Maria De Iorio, Fernando Quintana
Ann. Appl. Stat. 4(4): 2126-2149 (December 2010). DOI: 10.1214/10-AOAS368

Abstract

Based on a data set obtained in a dental longitudinal study, conducted in Flanders (Belgium), the joint time to caries distribution of permanent first molars was modeled as a function of covariates. This involves an analysis of multivariate continuous doubly-interval-censored data since: (i) the emergence time of a tooth and the time it experiences caries were recorded yearly, and (ii) events on teeth of the same child are dependent. To model the joint distribution of the emergence times and the times to caries, we propose a dependent Bayesian semiparametric model. A major feature of the proposed approach is that survival curves can be estimated without imposing assumptions such as proportional hazards, additive hazards, proportional odds or accelerated failure time.

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Alejandro Jara. Emmanuel Lesaffre. Maria De Iorio. Fernando Quintana. "Bayesian semiparametric inference for multivariate doubly-interval-censored data." Ann. Appl. Stat. 4 (4) 2126 - 2149, December 2010. https://doi.org/10.1214/10-AOAS368

Information

Published: December 2010
First available in Project Euclid: 4 January 2011

zbMATH: 1220.62023
MathSciNet: MR2829950
Digital Object Identifier: 10.1214/10-AOAS368

Keywords: Bayesian nonparametrics , linear dependent Dirichlet process prior , linear dependent Poisson–Dirichlet prior , Multivariate doubly-interval-censored data

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.4 • No. 4 • December 2010
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