阈值回归模型的变量选择及其在HIV药物依从性数据中的应用。

IF 0.8 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Statistics in Biosciences Pub Date : 2020-12-01 Epub Date: 2020-06-17 DOI:10.1007/s12561-020-09284-1
Takumi Saegusa, Tianzhou Ma, Gang Li, Ying Qing Chen, Mei-Ling Ting Lee
{"title":"阈值回归模型的变量选择及其在HIV药物依从性数据中的应用。","authors":"Takumi Saegusa,&nbsp;Tianzhou Ma,&nbsp;Gang Li,&nbsp;Ying Qing Chen,&nbsp;Mei-Ling Ting Lee","doi":"10.1007/s12561-020-09284-1","DOIUrl":null,"url":null,"abstract":"<p><p>The threshold regression model is an effective alternative to the Cox proportional hazards regression model when the proportional hazards assumption is not met. This paper considers variable selection for threshold regression. This model has separate regression functions for the initial health status and the speed of degradation in health. This flexibility is an important advantage when considering relevant risk factors for a complex time-to-event model where one needs to decide which variables should be included in the regression function for the initial health status, in the function for the speed of degradation in health, or in both functions. In this paper, we extend the broken adaptive ridge (BAR) method, originally designed for variable selection for one regression function, to simultaneous variable selection for both regression functions needed in the threshold regression model. We establish variable selection consistency of the proposed method and asymptotic normality of the estimator of non-zero regression coefficients. Simulation results show that our method outperformed threshold regression without variable selection and variable selection based on the Akaike information criterion. We apply the proposed method to data from an HIV drug adherence study in which electronic monitoring of drug intake is used to identify risk factors for non- adherence.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":"376-398"},"PeriodicalIF":0.8000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s12561-020-09284-1","citationCount":"4","resultStr":"{\"title\":\"Variable Selection in Threshold Regression Model with Applications to HIV Drug Adherence Data.\",\"authors\":\"Takumi Saegusa,&nbsp;Tianzhou Ma,&nbsp;Gang Li,&nbsp;Ying Qing Chen,&nbsp;Mei-Ling Ting Lee\",\"doi\":\"10.1007/s12561-020-09284-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The threshold regression model is an effective alternative to the Cox proportional hazards regression model when the proportional hazards assumption is not met. This paper considers variable selection for threshold regression. This model has separate regression functions for the initial health status and the speed of degradation in health. This flexibility is an important advantage when considering relevant risk factors for a complex time-to-event model where one needs to decide which variables should be included in the regression function for the initial health status, in the function for the speed of degradation in health, or in both functions. In this paper, we extend the broken adaptive ridge (BAR) method, originally designed for variable selection for one regression function, to simultaneous variable selection for both regression functions needed in the threshold regression model. We establish variable selection consistency of the proposed method and asymptotic normality of the estimator of non-zero regression coefficients. Simulation results show that our method outperformed threshold regression without variable selection and variable selection based on the Akaike information criterion. We apply the proposed method to data from an HIV drug adherence study in which electronic monitoring of drug intake is used to identify risk factors for non- adherence.</p>\",\"PeriodicalId\":45094,\"journal\":{\"name\":\"Statistics in Biosciences\",\"volume\":\" \",\"pages\":\"376-398\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1007/s12561-020-09284-1\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistics in Biosciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s12561-020-09284-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2020/6/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics in Biosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s12561-020-09284-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/6/17 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 4

摘要

当比例风险假设不满足时,阈值回归模型是Cox比例风险回归模型的有效替代。本文考虑阈值回归的变量选择问题。该模型具有初始健康状态和健康退化速度的独立回归函数。在考虑复杂的“时间到事件”模型的相关风险因素时,这种灵活性是一个重要的优势,因为需要决定哪些变量应包括在初始健康状态的回归函数中,哪些应包括在健康退化速度的函数中,或同时包括在这两个函数中。在本文中,我们扩展了破碎自适应脊(BAR)方法,最初设计用于一个回归函数的变量选择,以同时选择阈值回归模型所需的两个回归函数的变量。我们建立了该方法的变量选择一致性和非零回归系数估计量的渐近正态性。仿真结果表明,该方法优于无变量选择阈值回归和基于赤池信息准则的变量选择阈值回归。我们将提出的方法应用于艾滋病毒药物依从性研究的数据,其中使用电子监测药物摄入来识别不依从性的危险因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Variable Selection in Threshold Regression Model with Applications to HIV Drug Adherence Data.

The threshold regression model is an effective alternative to the Cox proportional hazards regression model when the proportional hazards assumption is not met. This paper considers variable selection for threshold regression. This model has separate regression functions for the initial health status and the speed of degradation in health. This flexibility is an important advantage when considering relevant risk factors for a complex time-to-event model where one needs to decide which variables should be included in the regression function for the initial health status, in the function for the speed of degradation in health, or in both functions. In this paper, we extend the broken adaptive ridge (BAR) method, originally designed for variable selection for one regression function, to simultaneous variable selection for both regression functions needed in the threshold regression model. We establish variable selection consistency of the proposed method and asymptotic normality of the estimator of non-zero regression coefficients. Simulation results show that our method outperformed threshold regression without variable selection and variable selection based on the Akaike information criterion. We apply the proposed method to data from an HIV drug adherence study in which electronic monitoring of drug intake is used to identify risk factors for non- adherence.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Statistics in Biosciences
Statistics in Biosciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
2.00
自引率
0.00%
发文量
28
期刊介绍: Statistics in Biosciences (SIBS) is published three times a year in print and electronic form. It aims at development and application of statistical methods and their interface with other quantitative methods, such as computational and mathematical methods, in biological and life science, health science, and biopharmaceutical and biotechnological science. SIBS publishes scientific papers and review articles in four sections, with the first two sections as the primary sections. Original Articles publish novel statistical and quantitative methods in biosciences. The Bioscience Case Studies and Practice Articles publish papers that advance statistical practice in biosciences, such as case studies, innovative applications of existing methods that further understanding of subject-matter science, evaluation of existing methods and data sources. Review Articles publish papers that review an area of statistical and quantitative methodology, software, and data sources in biosciences. Commentaries provide perspectives of research topics or policy issues that are of current quantitative interest in biosciences, reactions to an article published in the journal, and scholarly essays. Substantive science is essential in motivating and demonstrating the methodological development and use for an article to be acceptable. Articles published in SIBS share the goal of promoting evidence-based real world practice and policy making through effective and timely interaction and communication of statisticians and quantitative researchers with subject-matter scientists in biosciences.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信