title: Variable selection for mixture and promotion time cure rate models.
abstract: Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods. We illustrate the use of the methods using data from a study of childhood wheezing.
authors.affiliation: 1 Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA.
authors.forename: Abdullah
authors.initials: A
authors.lastname: Masud
authors.affiliation: 1 Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA.
authors.forename: Wanzhu
authors.initials: W
authors.lastname: Tu
authors.affiliation: 2 Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai, China.
authors.forename: Zhangsheng
authors.initials: Z
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002675:Child, Preschool; D002986:Clinical Trials as Topic; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D017063:Outcome Assessment, Health Care / Q000379:methods* / Q000706:statistics & numerical data; D012135:Respiratory Sounds
keywords: Bayesian information criterion; Mixture cure rate model; adaptive least absolute shrinkage and selection operators; expectation-maximization algorithm; promotion time cure rate model; wheeze