Variable selection for partially linear proportional hazards model with covariate measurement error. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Song, Xiao; Wang, Li; Ma, Shuangge; Huang, Hanwen
• Is Part Of: Journal of nonparametric statistics. Volume 31:Issue 1(2019)
• Page Start: 196
• Page End: 220
• Publication Date: 2019-01-02
• Subjects: Corrected score -- conditional score -- joint modelling -- polynomial spline -- survival; 62J07 -- 62G05 -- 62G20 -- 62N01; Nonparametric statistics -- Periodicals; 519.5
• DOI: 10.1080/10485252.2018.1545903
• Languages: English
• ISSNs: 1048-5252

Abstract

ABSTRACT: In survival analysis, we may encounter the following three problems: nonlinear covariate effect, variable selection and measurement error. Existing studies only address one or two of these problems. The goal of this study is to fill the knowledge gap and develop a novel approach to simultaneously address all three problems. Specifically, a partially time-varying coefficient proportional hazards model is proposed to more flexibly describe covariate effects. Corrected score and conditional score approaches are employed to accommodate potential measurement error. For the selection of relevant variables and regularised estimation, a penalisation approach is adopted. It is shown that the proposed approach has satisfactory asymptotic properties. It can be effectively realised using an iterative algorithm. The performance of the proposed approach is assessed via simulation studies and further illustrated by application to data from an AIDS clinical trial.