A Bayesian Analysis of Correlated Interval-Censored Data

Abstract

In epidemiological studies where subjects are seen periodically on follow-up visits, interval-censored data occur naturally. The exact time the change of state (such as HIV seroconversion) occurs is not known exactly, only that it occurred within some time interval. In multi-stage sampling or partner tracing studies, individuals are grouped into smaller subgroups. Individuals within a subgroup share an unobservable specific frailty which induces correlation within the subgroup. In this paper, we consider a Bayesian model for analysing correlated interval-censored data. Parameters are estimated using the Markov chain Monte Carlo methods, specifically the Gibbs sampler.

Keywords:

• Frailty
• Gibbs sampling
• Interval-censored
• Unobserved

Mathematics Subject Classification:

• 62P10
• 62N01
• 62N02
• 62F10
• 62F15

Acknowledgments

The author would like to thank Dr William M. Bolstad for his helpful and constructive comments. This publication was made possible in part through the support of the New Zealand Overseas Development Assistance.