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On the Plackett Distribution with Bivariate Censored Data

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  • Ghosh Debashis

    (Penn State University)

Abstract

In the analysis of dependence of bivariate correlated failure time data, a popular model is a gamma frailty model proposed by Clayton and Oakes. An alternative approach is using a Plackett distribution, whose dependence parameter has a very appealing odds ratio interpretation for dependence between the two failure times. In this article, we develop novel semiparametric estimation and inference procedures for the model. The asymptotic results of the estimator are developed; in addition, a goodness of fit test is also developed. We also discuss a regression extension to adjust for covariates using the linear regression model as well as applications to semi-competing risks data. The performance of the proposed techniques in finite samples is examined using simulation studies. Several real-data examples are used to illustrate the methodology.

Suggested Citation

  • Ghosh Debashis, 2008. "On the Plackett Distribution with Bivariate Censored Data," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-22, May.
    • Handle: RePEc:bpj:ijbist:v:4:y:2008:i:1:n:7
    DOI: 10.2202/1557-4679.1099
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    References listed on IDEAS

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    1. D. V. Glidden & S. G. Self, 1999. "Semiparametric Likelihood Estimation in the Clayton–Oakes Failure Time Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(3), pages 363-372, September.
    2. Tomasz Burzykowski & Geert Molenberghs & Marc Buyse & Helena Geys & Didier Renard, 2001. "Validation of surrogate end points in multiple randomized clinical trials with failure time end points," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(4), pages 405-422.
    3. Debashis Ghosh, 2006. "Semiparametric Global Cross‐ratio Models for Bivariate Censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(4), pages 609-619, December.
    4. Weijing Wang, 2003. "Estimating the association parameter for copula models under dependent censoring," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 257-273, February.
    5. Martin, Emily C. & Betensky, Rebecca A., 2005. "Testing Quasi-Independence of Failure and Truncation Times via Conditional Kendall's Tau," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 484-492, June.
    6. Lajmi Lakhal Chaieb & Louis-Paul Rivest & Belkacem Abdous, 2006. "Estimating survival under a dependent truncation," Biometrika, Biometrika Trust, vol. 93(3), pages 655-669, September.
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