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A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup
Sun, Liuquan1,2; Li, Shuwei1; Wang, Lianming3; Song, Xinyuan4
2021-07-01
Source PublicationSTATISTICAL METHODS IN MEDICAL RESEARCH
ISSN0962-2802
Pages14
AbstractFailure time data with a cured subgroup are frequently confronted in various scientific fields and many methods have been proposed for their analysis under right or interval censoring. However, a cure model approach does not seem to exist in the analysis of partly interval-censored data, which consist of both exactly observed and interval-censored observations on the failure time of interest. In this article, we propose a two-component mixture cure model approach for analyzing such type of data. We employ a logistic model to describe the cured probability and a proportional hazards model to model the latent failure time distribution for uncured subjects. We consider maximum likelihood estimation and develop a new expectation-maximization algorithm for its implementation. The asymptotic properties of the resulting estimators are established and the finite sample performance of the proposed method is examined through simulation studies. An application to a set of real data on childhood mortality in Nigeria is provided.
KeywordExpectation-maximization algorithm maximum likelihood estimation mixture cure model partly interval-censored data proportional hazards model
DOI10.1177/09622802211023985
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[11771431] ; National Natural Science Foundation of China[11690015] ; National Natural Science Foundation of China[11901128] ; Key Laboratory of RCSDS, CAS[2008DP173182] ; Natural Science Foundation of Guangdong Province of China[2021A1515010044] ; Science and Technology Program of Guangzhou of China[202102010512] ; Research Grant Council of the Hong Kong Special Administration Region[GRF 14301918] ; Research Grant Council of the Hong Kong Special Administration Region[14302519] ; National Institutes of Health[R01CA218578]
WOS Research AreaHealth Care Sciences & Services ; Mathematical & Computational Biology ; Medical Informatics ; Mathematics
WOS SubjectHealth Care Sciences & Services ; Mathematical & Computational Biology ; Medical Informatics ; Statistics & Probability
WOS IDWOS:000680119400001
PublisherSAGE PUBLICATIONS LTD
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/59027
Collection应用数学研究所
Corresponding AuthorLi, Shuwei
Affiliation1.Guangzhou Univ, Sch Econ & Stat, Guangzhou 510006, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing, Peoples R China
3.Univ South Carolina, Dept Stat, Columbia, SC USA
4.Chinese Univ Hong Kong, Dept Stat, Hong Kong, Peoples R China
Recommended Citation
GB/T 7714
Sun, Liuquan,Li, Shuwei,Wang, Lianming,et al. A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup[J]. STATISTICAL METHODS IN MEDICAL RESEARCH,2021:14.
APA Sun, Liuquan,Li, Shuwei,Wang, Lianming,&Song, Xinyuan.(2021).A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup.STATISTICAL METHODS IN MEDICAL RESEARCH,14.
MLA Sun, Liuquan,et al."A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup".STATISTICAL METHODS IN MEDICAL RESEARCH (2021):14.
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