Bayesian regression analysis of data with censored initiating and terminating times: Applications to aids

Xin M. Tu, Gang Jia, Jeanne Kowalski, Silviu A. Bacanu

Research output: Contribution to journalArticlepeer-review

Abstract

Data with censored initiating and terminating times arises quite frequently in acquired immunodeficiency syndrome (AIDS) epidemiologic studies. Analysis of such data involves a complicated bivariate likelihood, which is difficult to deal with computationally. Bayesian analysis, on the other hand, presents added complexities that have yet to be resolved. By exploiting the simple form of a complete data likelihood and utilizing the power of a Markov Chain Monte Carlo (MCMC) algorithm, this paper presents a methodology for fitting Bayesian regression models to such data. The proposed methods extend the work of Sinha (1997), who considered non-parametric Bayesian analysis of this type of data. The methodology is illustrated with an application to a cohort of HIV infected hemophiliac patients.

Original languageEnglish (US)
Pages (from-to)1-21
Number of pages21
JournalJournal of Statistical Computation and Simulation
Volume65
Issue number1
DOIs
StatePublished - Jan 1 2000

Keywords

  • Aids and hiv; bayesian regression analysis; interval censoring; mcmc algorithm

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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