Variable selection for mixture and promotion time cure rate models. (July 2018)

• Record Type: Journal Article
• Authors: Masud, Abdullah; Tu, Wanzhu; Yu, Zhangsheng
• Other Names: Dattner Itai guest-editor.; Huppert Amit guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 27:Number 7(2018)
• Page Start: 2185
• Page End: 2199
• Publication Date: 2018-07
• Subjects: Mixture cure rate model -- promotion time cure rate model -- adaptive least absolute shrinkage and selection operators -- expectation-maximization algorithm -- Bayesian information criterion -- wheeze; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280216677748
• Languages: English
• ISSNs: 0962-2802

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.