Feature screening for case-cohort studies in the presence of interval censoring

Zhimiao Cao, Huiqiong Li, Jianguo Sun and Niansheng Tang

Journal of Nonparametric Statistics, 2025, vol. 37, issue 3, 598-631

Abstract: A great literature has been established for both feature screening under various situations and the analysis of case-cohort studies. Also several methods have been proposed for feature screening when one faces case-cohort studies but all of the existing methods apply only to right-censored failure time data. In this paper, we discuss feature screening for case-cohort studies in the presence of interval censoring, a type of censoring that occurs more generally and includes right censoring as a special case. For the problem, two methods, a model-based procedure and a model-free procedure, are proposed. The former, an inverse probability weighted joint likelihood screening approach, is developed under the proportional hazards model, the most commonly used model for failure time data analysis. The latter is a weighted smooth partial rank screening method that allows for both ultrahigh-dimensional covariates and covariate-dependent censoring. For the proposed methods, the sure screening properties are established under mild regularity conditions. An extensive simulation study is conducted to assess the performance of the proposed methods and indicates that they work well in practical situations.

Date: 2025
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DOI: 10.1080/10485252.2024.2429541

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