Literature DB >> 27856963

Variable selection for mixture and promotion time cure rate models.

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


  2 in total

1.  Evaluation of the Factors Affecting the Cure Rate of Cervical Intra-Epithelial Neoplasia Recurrence Using Defective Models.

Authors:  Nastaran Hajizadeh; Ahmad Reza Baghestani; Mohamad Amin Pourhoseingholi; Ali Akbar Khadem Maboudi; Farah Farzaneh; Nafiseh Faghih
Journal:  J Res Health Sci       Date:  2021-07-12

2.  Controlled variable selection in Weibull mixture cure models for high-dimensional data.

Authors:  Han Fu; Deedra Nicolet; Krzysztof Mrózek; Richard M Stone; Ann-Kathrin Eisfeld; John C Byrd; Kellie J Archer
Journal:  Stat Med       Date:  2022-07-06       Impact factor: 2.497

  2 in total

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