KMS Of Academy of mathematics and systems sciences, CAS
| 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
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| 2021-07-01 | |
| Source Publication | STATISTICAL METHODS IN MEDICAL RESEARCH
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| ISSN | 0962-2802 |
| Pages | 14 |
| Abstract | 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. |
| Keyword | Expectation-maximization algorithm maximum likelihood estimation mixture cure model partly interval-censored data proportional hazards model |
| DOI | 10.1177/09622802211023985 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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 Research Area | Health Care Sciences & Services ; Mathematical & Computational Biology ; Medical Informatics ; Mathematics |
| WOS Subject | Health Care Sciences & Services ; Mathematical & Computational Biology ; Medical Informatics ; Statistics & Probability |
| WOS ID | WOS:000680119400001 |
| Publisher | SAGE PUBLICATIONS LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/59027 |
| Collection | 应用数学研究所 |
| Corresponding Author | Li, Shuwei |
| Affiliation | 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 |
| 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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