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

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

doi:10.1080/00949650008811987

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

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

School of Medicine

Research output: Contribution to journal › Article › peer-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 language	English (US)
Pages (from-to)	1-21
Number of pages	21
Journal	Journal of Statistical Computation and Simulation
Volume	65
Issue number	1
DOIs	https://doi.org/10.1080/00949650008811987
State	Published - 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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10.1080/00949650008811987

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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.",

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AB - 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.

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

Abstract

Keywords

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