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Censored linear regression for case-cohort studies

Author

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  • Bin Nan
  • Menggang Yu
  • John D. Kalbfleisch

Abstract

Right-censored data from a classical case-cohort design and a stratified case-cohort design are considered. In the classical case-cohort design the subcohort is obtained as a simple random sample of the entire cohort, whereas in the stratified design this subcohort is elected by independent Bernoulli sampling with arbitrary selection probabilities. For each design and under a linear regression model, methods for estimating the regression parameters are proposed and analysed. These methods are derived by modifying the linear ranks tests and estimating equations that arise from full-cohort data using methods that are similar to the pseudolikelihood estimating equation that has been used in relative risk regression for these models. The estimators so obtained are shown to be consistent and asymptotically normal. Variance estimation and numerical illustrations are also provided. Copyright 2006, Oxford University Press.

Suggested Citation

  • Bin Nan & Menggang Yu & John D. Kalbfleisch, 2006. "Censored linear regression for case-cohort studies," Biometrika, Biometrika Trust, vol. 93(4), pages 747-762, December.
    • Handle: RePEc:oup:biomet:v:93:y:2006:i:4:p:747-762
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    File URL: http://hdl.handle.net/10.1093/biomet/93.4.747
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Mingzhe Wu & Ming Zheng & Wen Yu & Ruofan Wu, 2018. "Estimation and variable selection for semiparametric transformation models under a more efficient cohort sampling design," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 570-596, September.
    4. Jichang Yu & Haibo Zhou & Jianwen Cai, 2021. "Accelerated failure time model for data from outcome-dependent sampling," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(1), pages 15-37, January.
    5. Menggang Yu & Bin Nan, 2010. "Regression Calibration in Semiparametric Accelerated Failure Time Models," Biometrics, The International Biometric Society, vol. 66(2), pages 405-414, June.
    6. 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.
    7. 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.
    8. Jon Arni Steingrimsson & Robert L. Strawderman, 2017. "Estimation in the Semiparametric Accelerated Failure Time Model With Missing Covariates: Improving Efficiency Through Augmentation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1221-1235, July.

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