Variable selection for mixture and promotion time cure rate models

Abdullah Masud, Wanzhu Tu, Zhangsheng Yu

Research output: Contribution to journalArticle

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2185-2199
Number of pages15
JournalStatistical Methods in Medical Research
Volume27
Issue number7
DOIs
StatePublished - Jul 1 2018

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Keywords

  • adaptive least absolute shrinkage and selection operators
  • Bayesian information criterion
  • expectation-maximization algorithm
  • Mixture cure rate model
  • promotion time cure rate model
  • wheeze

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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