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Weighted estimating equations for semiparametric transformation models with censored data from a case-cohort design

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  • Lan Kong

Abstract

In a case-cohort design introduced by Prentice (1986), covariates are assembled only for a subcohort randomly selected from the entire cohort, and any additional cases outside the subcohort. Semiparametric transformation models are considered here for failure time data from the case-cohort design. Weighted estimating equations are proposed for estimation of the regression parameters. The estimation procedure of survival probability at given covariate levels is also provided. Asymptotic properties are derived for the estimators using finite population sampling theory, U-statistics theory and martingale convergence results. The finite-sample properties of the proposed estimators, as well as the efficiency relative to the full cohort estimators, are assessed via simulation studies. A case-cohort dataset from the Atherosclerosis Risk in Communities study is used to illustrate the estimating procedure. Copyright Biometrika Trust 2004, Oxford University Press.

Suggested Citation

  • Lan Kong, 2004. "Weighted estimating equations for semiparametric transformation models with censored data from a case-cohort design," Biometrika, Biometrika Trust, vol. 91(2), pages 305-319, June.
    • Handle: RePEc:oup:biomet:v:91:y:2004:i:2:p:305-319
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    Cited by:

    1. Weibin Zhong & Guoqing Diao, 2023. "Joint semiparametric models for case‐cohort designs," Biometrics, The International Biometric Society, vol. 79(3), pages 1959-1971, September.
    2. Lan Kong & Jianwen Cai, 2009. "Case–Cohort Analysis with Accelerated Failure Time Model," Biometrics, The International Biometric Society, vol. 65(1), pages 135-142, March.
    3. Yichen Lou & Peijie Wang & Jianguo Sun, 2023. "A semi-parametric weighted likelihood approach for regression analysis of bivariate interval-censored outcomes from case-cohort studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 628-653, July.
    4. Jieli Ding & Tsui-Shan Lu & Jianwen Cai & Haibo Zhou, 2017. "Recent progresses in outcome-dependent sampling with failure time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 57-82, January.
    5. Shou-En Lu & Joanna H. Shih, 2006. "Case-Cohort Designs and Analysis for Clustered Failure Time Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1138-1148, December.
    6. Zhao, Yichuan, 2010. "Semiparametric inference for transformation models via empirical likelihood," Journal of Multivariate Analysis, Elsevier, vol. 101(8), pages 1846-1858, September.
    7. Wenbin Lu & Lexin Li, 2011. "Sufficient Dimension Reduction for Censored Regressions," Biometrics, The International Biometric Society, vol. 67(2), pages 513-523, June.
    8. Suhyun Kang & Wenbin Lu & Mengling Liu, 2017. "Efficient estimation for accelerated failure time model under case-cohort and nested case-control sampling," Biometrics, The International Biometric Society, vol. 73(1), pages 114-123, March.
    9. Hui Zhang & Douglas E. Schaubel & John D. Kalbfleisch, 2011. "Proportional Hazards Regression for the Analysis of Clustered Survival Data from Case–Cohort Studies," Biometrics, The International Biometric Society, vol. 67(1), pages 18-28, March.

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