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Bayesian proportional hazards model for current status data with monotone splines

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  • Cai, Bo
  • Lin, Xiaoyan
  • Wang, Lianming

Abstract

The proportional hazards model is widely used to deal with time to event data in many fields. However, its popularity is limited to right-censored data, for which the partial likelihood is available and the partial likelihood method allows one to estimate the regression coefficients directly without estimating the baseline hazard function. In this paper, we focus on current status data and propose an efficient and easy-to-implement Bayesian approach under the proportional hazards model. Specifically, we model the baseline cumulative hazard function with monotone splines leading to only a finite number of parameters to estimate while maintaining great modeling flexibility. An efficient Gibbs sampler is proposed for posterior computation relying on a data augmentation through Poisson latent variables. The proposed method is evaluated and compared to a constrained maximum likelihood method and three other existing approaches in a simulation study. Uterine fibroid data from an epidemiological study are analyzed as an illustration.

Suggested Citation

  • Cai, Bo & Lin, Xiaoyan & Wang, Lianming, 2011. "Bayesian proportional hazards model for current status data with monotone splines," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2644-2651, September.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:9:p:2644-2651
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    References listed on IDEAS

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    1. W. R. Gilks & N. G. Best & K. K. C. Tan, 1995. "Adaptive Rejection Metropolis Sampling Within Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(4), pages 455-472, December.
    2. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
    3. Mongoué-Tchokoté, Solange & Kim, Jong-Sung, 2008. "New statistical software for the proportional hazards model with current status data," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4272-4286, May.
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    Citations

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    Cited by:

    1. Minggen Lu & Christopher S. McMahan, 2018. "A partially linear proportional hazards model for current status data," Biometrics, The International Biometric Society, vol. 74(4), pages 1240-1249, December.
    2. Chunling Wang & Xiaoyan Lin, 2022. "Bayesian Semiparametric Regression Analysis of Multivariate Panel Count Data," Stats, MDPI, vol. 5(2), pages 1-17, May.
    3. Pan, Chun & Cai, Bo & Wang, Lianming & Lin, Xiaoyan, 2014. "Bayesian semiparametric model for spatially correlated interval-censored survival data," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 198-208.
    4. Prabhashi W. Withana Gamage & Monica Chaudari & Christopher S. McMahan & Edwin H. Kim & Michael R. Kosorok, 2020. "An extended proportional hazards model for interval-censored data subject to instantaneous failures," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(1), pages 158-182, January.
    5. Jianhong Wang & Xiaoyan Lin, 2020. "A Bayesian approach for semiparametric regression analysis of panel count data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(2), pages 402-420, April.
    6. Prabhashi W. Withana Gamage & Christopher S. McMahan & Lianming Wang, 2023. "A flexible parametric approach for analyzing arbitrarily censored data that are potentially subject to left truncation under the proportional hazards model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 188-212, January.
    7. Yao, Bin & Wang, Lianming & He, Xin, 2016. "Semiparametric regression analysis of panel count data allowing for within-subject correlation," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 47-59.
    8. Wang, Naichen & Wang, Lianming & McMahan, Christopher S., 2015. "Regression analysis of bivariate current status data under the Gamma-frailty proportional hazards model using the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 140-150.
    9. Hao Liu & Jing Qin, 2018. "Semiparametric probit models with univariate and bivariate current†status data," Biometrics, The International Biometric Society, vol. 74(1), pages 68-76, March.
    10. Jochen Ranger & Jörg-Tobias Kuhn, 2015. "Modeling Information Accumulation in Psychological Tests Using Item Response Times," Journal of Educational and Behavioral Statistics, , vol. 40(3), pages 274-306, June.
    11. Kaeding, Matthias, 2020. "Efficient Bayesian nonparametric hazard regression," Ruhr Economic Papers 850, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    12. Gamage, Prabhashi W. Withana & McMahan, Christopher S. & Wang, Lianming & Tu, Wanzhu, 2018. "A Gamma-frailty proportional hazards model for bivariate interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 354-366.

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