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Variable selection for time-varying effects based on interval-censored failure time data
Statistics and Its Interface ( IF 0.8 ) Pub Date : 2022-02-14 , DOI: 10.4310/21-sii687
Kaiyi Chen 1 , Jianguo Sun 1
Affiliation  

Variable selection has recently attracted a great deal of attention and correspondingly, many methods have been proposed. In this paper, we discuss the topic when one faces interval-censored failure time data arising from a model with time-varying coefficients, for which there does not seem to exist a method. For the situation, in addition to identifying important variables or covariates, a desired feature of a variable selection method is to distinguish time-varying coefficients from time-independent ones, which also presents an additional challenge. To address these, a penalized maximum likelihood procedure is presented and in the proposed method, the adaptive group Lasso penalty function and B‑spline functions are used. The approach can simultaneously select between time-dependent and time-independent covariate effects. To implement the proposed procedure, an EM algorithm is developed, and a simulation study is conducted and suggests that the proposed method works well in practical situations. Finally it is applied a set of real data on Alzheimer’s disease that motivated this study.

中文翻译:

基于区间删失失效时间数据的时变效应变量选择

变量选择最近引起了很多关注,相应地,已经提出了许多方法。在本文中,我们讨论了当一个人面对由具有时变系数的模型产生的区间删失失效时间数据时的主题,对于这种模型似乎不存在一种方法。对于这种情况,除了识别重要的变量或协变量外,变量选择方法的一个期望特征是区分时变系数和与时间无关的系数,这也带来了额外的挑战。为了解决这些问题,提出了一种惩罚最大似然程序,并且在所提出的方法中,使用了自适应组 Lasso 惩罚函数和 B 样条函数。该方法可以同时在时间相关和时间无关的协变量效应之间进行选择。为了实现所提出的程序,开发了一种 EM 算法,并进行了仿真研究,表明所提出的方法在实际情况下效果很好。最后,它应用了一组关于阿尔茨海默病的真实数据,激发了这项研究。
更新日期:2022-02-15
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