Bi-level variable selection in semiparametric transformation mixture cure models for right-censored data

We investigate the bi-level variable selection problem in semiparametric transformation mixture cure models (STMCM). In this type of mixture cure models, we consider a class of semiparametric transformation models for the conditional survival function and a logistic regression for the incidence comp...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 52; no. 7; pp. 3006 - 3025
Main Authors Wu, Jingjing, Lu, Xuewen, Zhong, Wenyan
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 03.07.2023
Taylor & Francis Ltd
Subjects
Bi-level variable selection
Censored data (mathematics)
Group bridge
Mixture cure model
Mixtures
Penalized regression
Primary 62N01
secondary 62J07
Semiparametric transformation model
Survival
Transformations
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Summary:We investigate the bi-level variable selection problem in semiparametric transformation mixture cure models (STMCM). In this type of mixture cure models, we consider a class of semiparametric transformation models for the conditional survival function and a logistic regression for the incidence component, then conduct group variable selection. The group bridge penalty is adopted for bi-level variable selection on both parts of the mixture cure models. Through simulation studies and real data analyses, we show that the proposed method can identify the important variables and groups that contribute to the cure proportion and the survival function for the uncured subjects, respectively.
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ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2021.1926499