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Bootstrap for the case-cohort design

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  • Yijian Huang

Abstract

The case-cohort design facilitates economical investigation of risk factors in a large survival study, with covariate data collected only from the cases and a simple random subset of the full cohort. Methods that accommodate the design have been developed for various semiparametric models, but most inference procedures are based on asymptotic distribution theory. Such inference can be cumbersome to derive and implement, and does not permit confidence band construction. While the bootstrap is an obvious alternative, it is unclear how to resample because of complications from the two-stage sampling design. We establish an equivalent sampling scheme, and propose a novel and versatile nonparametric bootstrap for robust inference with an appealingly simple single-stage resampling. Theoretical justification and numerical assessment are provided for a number of procedures under the proportional hazards model.

Suggested Citation

  • Yijian Huang, 2014. "Bootstrap for the case-cohort design," Biometrika, Biometrika Trust, vol. 101(2), pages 465-476.
    • Handle: RePEc:oup:biomet:v:101:y:2014:i:2:p:465-476.
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    File URL: http://hdl.handle.net/10.1093/biomet/asu004
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    Cited by:

    1. Yayun Xu & Soyoung Kim & Mei-Jie Zhang & David Couper & Kwang Woo Ahn, 2022. "Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 241-262, April.
    2. J. E. Soh & Yijian Huang, 2021. "A varying-coefficient model for gap times between recurrent events," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(3), pages 437-459, July.
    3. Rebecca Payne & Ming Yang & Yingye Zheng & Majken K. Jensen & Tianxi Cai, 2016. "Robust risk prediction with biomarkers under two‐phase stratified cohort design," Biometrics, The International Biometric Society, vol. 72(4), pages 1037-1045, December.

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