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A Bayesian MCMC approach to survival analysis with doubly-censored data

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  • Yu, Binbing

Abstract

Doubly-censored data refers to time to event data for which both the originating and failure times are censored. In studies involving AIDS incubation time or survival after dementia onset, for example, data are frequently doubly-censored because the date of the originating event is interval-censored and the date of the failure event usually is right-censored. The primary interest is in the distribution of elapsed times between the originating and failure events and its relationship to exposures and risk factors. The estimating equation approach [Sun et al. (1999). Regression analysis of doubly censored failure time data with applications to AIDS studies. Biometrics 55, 909-914] and its extensions assume the same distribution of originating event times for all subjects. This paper demonstrates the importance of utilizing additional covariates to impute originating event times, i.e., more accurate estimation of originating event times may lead to less biased parameter estimates for elapsed time. The Bayesian MCMC method is shown to be a suitable approach for analyzing doubly-censored data and allows a rich class of survival models. The performance of the proposed estimation method is compared to that of other conventional methods through simulations. Two examples, an AIDS cohort study and a population-based dementia study, are used for illustration. Sample code is shown in [Appendix A WinBUGS code for the dementia survival analysis] and [Appendix B Data and WinBUGS code for the AIDS analysis].

Suggested Citation

  • Yu, Binbing, 2010. "A Bayesian MCMC approach to survival analysis with doubly-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1921-1929, August.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:8:p:1921-1929
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    2. Jianguo Sun & Qiming Liao & Marcello Pagano, 1999. "Regression Analysis of Doubly Censored Failure Time Data with Applications to AIDS Studies," Biometrics, The International Biometric Society, vol. 55(3), pages 909-914, September.
    3. Liuquan Sun & Yang-jin Kim & Jianguo Sun, 2004. "Regression Analysis of Doubly Censored Failure Time Data Using the Additive Hazards Model," Biometrics, The International Biometric Society, vol. 60(3), pages 637-643, September.
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    6. Wei Pan, 2001. "A Multiple Imputation Approach to Regression Analysis for Doubly Censored Data with Application to AIDS Studies," Biometrics, The International Biometric Society, vol. 57(4), pages 1245-1250, December.
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    1. Oludare Samuel Ariyo & Matthew Adekunle Adeleke, 2022. "Simultaneous Bayesian modelling of skew-normal longitudinal measurements with non-ignorable dropout," Computational Statistics, Springer, vol. 37(1), pages 303-325, March.

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