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Cox regression in cohort studies with validation sampling

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  • Yi‐Hau Chen

Abstract

An estimation procedure is proposed for the Cox model in cohort studies with validation sampling, where crude covariate information is observed for the full cohort and true covariate information is collected on a validation set sampled randomly from the full cohort. The method proposed makes use of the partial information from data that are available on the entire cohort by fitting a working Cox model relating crude covariates to the failure time. The resulting estimator is consistent regardless of the specification of the working model and is asymptotically more efficient than the validation‐set‐only estimator. Approximate asymptotic relative efficiencies with respect to some alternative methods are derived under a simple scenario and further studied numerically. The finite sample performance is investigated and compared with alternative methods via simulation studies. A similar procedure also works for the case where the validation set is a stratified random sample from the cohort.

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  • Yi‐Hau Chen, 2002. "Cox regression in cohort studies with validation sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(1), pages 51-62, January.
    • Handle: RePEc:bla:jorssb:v:64:y:2002:i:1:p:51-62
    DOI: 10.1111/1467-9868.00324
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    Cited by:

    1. Ying Yan & Haibo Zhou & Jianwen Cai, 2017. "Improving efficiency of parameter estimation in case-cohort studies with multivariate failure time data," Biometrics, The International Biometric Society, vol. 73(3), pages 1042-1052, September.
    2. Wang, Xuan & Wang, Qihua, 2015. "Semiparametric linear transformation model with differential measurement error and validation sampling," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 67-80.
    3. Sehee Kim & Yi Li & Donna Spiegelman, 2016. "A semiparametric copula method for Cox models with covariate measurement error," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 1-16, January.
    4. 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.
    5. Cao, Yongxiu & Yu, Jichang, 2023. "Adjusting for unmeasured confounding in survival causal effect using validation data," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    6. Mengling Liu & Wenbin Lu & Chi-hong Tseng, 2010. "Cox Regression in Nested Case–Control Studies with Auxiliary Covariates," Biometrics, The International Biometric Society, vol. 66(2), pages 374-381, June.
    7. Wang, Qihua & Zhang, Riquan, 2009. "Statistical estimation in varying coefficient models with surrogate data and validation sampling," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2389-2405, November.
    8. Wang, Qihua, 2006. "Nonparametric regression function estimation with surrogate data and validation sampling," Journal of Multivariate Analysis, Elsevier, vol. 97(5), pages 1142-1161, May.

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