{"title":"带有异质性参数的比例危险模型下用于高维受控变量选择的 Model-X Knockoffs","authors":"Ran Hu, Di Xia, Haoyu Wang, Caixu Xu, Yingli Pan","doi":"10.1007/s00184-024-00966-0","DOIUrl":null,"url":null,"abstract":"<p>A major challenge arising from data integration pertains to data heterogeneity in terms of study population, study design, or study coordination. Ignoring such heterogeneity in data analysis can lead to the biased estimation. In this paper, regression analysis of the proportional hazards model with heterogeneity parameter is studied. We combine the Model-X Knockoffs procedure with fused LASSO approach to control the false discovery rate in the variable selection and learn the integrative data analysis of partially heterogeneous subgroups when the outcome of interest is time to event. A regularized working partial likelihood function is established and a trick of reparameterization is developed for the numerical calculation of the proposed estimator. Simulation studies are conducted to assess the finite-sample performance of the proposed method. A data example from a clinical trial in primary biliary cirrhosis study is analyzed to demonstrate the application of our proposed method.</p>","PeriodicalId":49821,"journal":{"name":"Metrika","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Model-X Knockoffs for high-dimensional controlled variable selection under the proportional hazards model with heterogeneity parameter\",\"authors\":\"Ran Hu, Di Xia, Haoyu Wang, Caixu Xu, Yingli Pan\",\"doi\":\"10.1007/s00184-024-00966-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>A major challenge arising from data integration pertains to data heterogeneity in terms of study population, study design, or study coordination. Ignoring such heterogeneity in data analysis can lead to the biased estimation. In this paper, regression analysis of the proportional hazards model with heterogeneity parameter is studied. We combine the Model-X Knockoffs procedure with fused LASSO approach to control the false discovery rate in the variable selection and learn the integrative data analysis of partially heterogeneous subgroups when the outcome of interest is time to event. A regularized working partial likelihood function is established and a trick of reparameterization is developed for the numerical calculation of the proposed estimator. Simulation studies are conducted to assess the finite-sample performance of the proposed method. A data example from a clinical trial in primary biliary cirrhosis study is analyzed to demonstrate the application of our proposed method.</p>\",\"PeriodicalId\":49821,\"journal\":{\"name\":\"Metrika\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-05-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Metrika\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1007/s00184-024-00966-0\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metrika","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s00184-024-00966-0","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Model-X Knockoffs for high-dimensional controlled variable selection under the proportional hazards model with heterogeneity parameter
A major challenge arising from data integration pertains to data heterogeneity in terms of study population, study design, or study coordination. Ignoring such heterogeneity in data analysis can lead to the biased estimation. In this paper, regression analysis of the proportional hazards model with heterogeneity parameter is studied. We combine the Model-X Knockoffs procedure with fused LASSO approach to control the false discovery rate in the variable selection and learn the integrative data analysis of partially heterogeneous subgroups when the outcome of interest is time to event. A regularized working partial likelihood function is established and a trick of reparameterization is developed for the numerical calculation of the proposed estimator. Simulation studies are conducted to assess the finite-sample performance of the proposed method. A data example from a clinical trial in primary biliary cirrhosis study is analyzed to demonstrate the application of our proposed method.
期刊介绍:
Metrika is an international journal for theoretical and applied statistics. Metrika publishes original research papers in the field of mathematical statistics and statistical methods. Great importance is attached to new developments in theoretical statistics, statistical modeling and to actual innovative applicability of the proposed statistical methods and results. Topics of interest include, without being limited to, multivariate analysis, high dimensional statistics and nonparametric statistics; categorical data analysis and latent variable models; reliability, lifetime data analysis and statistics in engineering sciences.