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Variable selection for partially linear proportional hazards model with covariate measurement error
Journal of Nonparametric Statistics ( IF 1.2 ) Pub Date : 2018-11-14 , DOI: 10.1080/10485252.2018.1545903
Xiao Song 1 , Li Wang 2 , Shuangge Ma 3 , Hanwen Huang 1
Affiliation  

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.

中文翻译:

具有协变量测量误差的部分线性比例风险模型的变量选择

摘要 在生存分析中,我们可能会遇到以下三个问题:非线性协变量效应、变量选择和测量误差。现有的研究仅解决了其中一两个问题。本研究的目标是填补知识空白并开发一种新方法来同时解决所有三个问题。具体而言,提出了一个部分时变系数比例风险模型,以更灵活地描述协变量效应。采用修正分数和条件分数方法来适应潜在的测量误差。对于相关变量的选择和正则化估计,采用了惩罚的方法。结果表明,所提出的方法具有令人满意的渐近特性。它可以使用迭代算法有效地实现。
更新日期:2018-11-14
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