A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup

doi:10.1177/09622802211023985

CSpace > 应用数学研究所

	A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup
	Sun, Liuquan1,2 ; Li, Shuwei 1; Wang, Lianming 3; Song, Xinyuan 4
	2021-07-01
发表期刊	STATISTICAL METHODS IN MEDICAL RESEARCH
ISSN	0962-2802
页码	14
摘要	Failure 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.
关键词	Expectation-maximization algorithm maximum likelihood estimation mixture cure model partly interval-censored data proportional hazards model
DOI	10.1177/09622802211023985
收录类别	SCI
语种	英语
资助项目	National 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研究方向	Health Care Sciences & Services ; Mathematical & Computational Biology ; Medical Informatics ; Mathematics
WOS类目	Health Care Sciences & Services ; Mathematical & Computational Biology ; Medical Informatics ; Statistics & Probability
WOS记录号	WOS:000680119400001
出版者	SAGE PUBLICATIONS LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/59027
专题	应用数学研究所
通讯作者	Li, Shuwei
作者单位	1.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
推荐引用方式 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.