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A Bayesian approach for analyzing case 2 interval-censored data under the semiparametric proportional odds model

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  • Wang, Lianming
  • Lin, Xiaoyan

Abstract

Analyzing interval-censored data is difficult due to its complex data structure containing left-, interval-, and right-censored observations. An easy-to-implement Bayesian approach is proposed under the proportional odds (PO) model for analyzing such data. The nondecreasing baseline log odds function is modeled with a linear combination of monotone splines. Two efficient Gibbs samplers are developed based on two different data augmentations using the relationship between the PO model and the logistic distribution. In the first data augmentation, the logistic distribution is achieved by the scaled normal mixture with the scale parameter related to the Kolmogorov-Smirnove distribution. In the second data augmentation, the logistic distribution is approximated by a Student's t distribution up to a scale constant. The proposed methods are evaluated by simulation studies and illustrated with an application of an HIV data set.

Suggested Citation

  • Wang, Lianming & Lin, Xiaoyan, 2011. "A Bayesian approach for analyzing case 2 interval-censored data under the semiparametric proportional odds model," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 876-883, July.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:7:p:876-883
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    References listed on IDEAS

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    1. Daniel Rabinowitz & Rebecca A. Betensky & Anastasios A. Tsiatis, 2000. "Using Conditional Logistic Regression to Fit Proportional Odds Models to Interval Censored Data," Biometrics, The International Biometric Society, vol. 56(2), pages 511-518, June.
    2. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    3. Timothy Hanson & Mingan Yang, 2007. "Bayesian Semiparametric Proportional Odds Models," Biometrics, The International Biometric Society, vol. 63(1), pages 88-95, March.
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    Cited by:

    1. 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.
    2. Guo-Liang Tian & Mingqiu Wang & Lixin Song, 2014. "Variable selection in the high-dimensional continuous generalized linear model with current status data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(3), pages 467-483, March.

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