Premium
Variable selection for frailty transformation models with application to diabetic complications
Author(s) -
Liu Xu,
Song Xinyuan,
Xie Shangyu,
Zhou Yong
Publication year - 2016
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11291
Subject(s) - selection (genetic algorithm) , transformation (genetics) , variable (mathematics) , feature selection , computer science , diabetes mellitus , medicine , artificial intelligence , mathematics , biology , genetics , mathematical analysis , gene , endocrinology
This article focuses on variable selection in the context of gamma frailty transformation models for multivariate survival times. We propose a method based on a nonconcave penalty function to select relevant regression predictors and simultaneously estimate model parameters and nonparametric functions. Our procedure performs as well as the oracle one in which the true model is assumed to be known. We conduct simulation studies to demonstrate the performance of the proposed methodology. We apply our method to a case study based on the Hong Kong Diabetes Registry, and obtain new insights on the risk factors of cardiovascular–renal complications for type 2 diabetic patients. The Canadian Journal of Statistics 44: 375–394; 2016 © 2016 Statistical Society of Canada