The Adjustment of Covariates in Cox’s Model under Case-Cohort Design

Guocai Rong, Luwei Tang, Wenting Luo, Qing Li and Lifeng Deng

Complexity, 2020, vol. 2020, 1-16

Abstract:

Case-cohort design is a biased sampling method. Due to its cost-effective and theoretical significance, this design has extensive application value in many large cohort studies. The case-cohort data includes a subcohort sampled randomly from the entire cohort and all the failed subjects outside the subcohort. In this paper, the adjustment for the distorted covariates is considered to case-cohort data in Cox’s model. According to the existing adjustable methods of distorted covariates for linear and nonlinear models, we propose estimating the distorting functions by nonparametrically regressing the distorted covariates on the distorting factors; then, the estimators for the parameters are obtained using the estimated covariates. The proof of consistency and being asymptotically normal is completed. For calculating the maximum likelihood estimates of the regression coefficients subject in Cox’s model, a minorization-maximization (MM) algorithm is developed. Simulation studies are performed to compare the estimations with the covariates undistorted, distorted, and adjusted to illustrate the proposed methods.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:8884665

DOI: 10.1155/2020/8884665

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