| Literature DB >> 27856963 |
Abdullah Masud1, Wanzhu Tu1, Zhangsheng Yu2.
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.Entities:
Keywords: Bayesian information criterion; Mixture cure rate model; adaptive least absolute shrinkage and selection operators; expectation-maximization algorithm; promotion time cure rate model; wheeze
Mesh:
Year: 2016 PMID: 27856963 DOI: 10.1177/0962280216677748
Source DB: PubMed Journal: Stat Methods Med Res ISSN: 0962-2802 Impact factor: 3.021