Variable selection for mixture and promotion time cure rate models

Abdullah Masud, Wanzhu Tu, Zhangsheng Yu

Research output: Contribution to journalArticle

2 Citations (Scopus)

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

Fingerprint

Cure Rate Model
Variable Selection
Cure Model
Failure Time Data
Operating Characteristics
Respiratory Sounds
Shrinkage
Hazard
Baseline
Simulation Study
Promotion
Operator
Research

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

Cite this

Variable selection for mixture and promotion time cure rate models. / Masud, Abdullah; Tu, Wanzhu; Yu, Zhangsheng.

In: Statistical Methods in Medical Research, Vol. 27, No. 7, 01.07.2018, p. 2185-2199.

Research output: Contribution to journalArticle

@article{c873d252f7a44e62945a6e3ef2332df9,
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.",
keywords = "adaptive least absolute shrinkage and selection operators, Bayesian information criterion, expectation-maximization algorithm, Mixture cure rate model, promotion time cure rate model, wheeze",
author = "Abdullah Masud and Wanzhu Tu and Zhangsheng Yu",
year = "2018",
month = "7",
day = "1",
doi = "10.1177/0962280216677748",
language = "English (US)",
volume = "27",
pages = "2185--2199",
journal = "Statistical Methods in Medical Research",
issn = "0962-2802",
publisher = "SAGE Publications Ltd",
number = "7",

}

TY - JOUR

T1 - Variable selection for mixture and promotion time cure rate models

AU - Masud, Abdullah

AU - Tu, Wanzhu

AU - Yu, Zhangsheng

PY - 2018/7/1

Y1 - 2018/7/1

N2 - 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.

AB - 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.

KW - adaptive least absolute shrinkage and selection operators

KW - Bayesian information criterion

KW - expectation-maximization algorithm

KW - Mixture cure rate model

KW - promotion time cure rate model

KW - wheeze

UR - http://www.scopus.com/inward/record.url?scp=85048002392&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048002392&partnerID=8YFLogxK

U2 - 10.1177/0962280216677748

DO - 10.1177/0962280216677748

M3 - Article

AN - SCOPUS:85048002392

VL - 27

SP - 2185

EP - 2199

JO - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

SN - 0962-2802

IS - 7

ER -