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DC Field | Value | Language |
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dc.contributor.author | Li, E | - |
dc.contributor.author | Yu, K | - |
dc.contributor.author | Tang, ML | - |
dc.contributor.author | Tian, M | - |
dc.date.accessioned | 2024-03-12T16:24:01Z | - |
dc.date.available | 2024-03-12T16:24:01Z | - |
dc.date.issued | 2025-01-17 | - |
dc.identifier | ORCiD: Man-Lai Tang https://orcid.org/0000-0003-3934-2676 | - |
dc.identifier | ORCiD: Keming Yu https://orcid.org/0000-0001-6341-8402 | - |
dc.identifier.citation | Li, E. et al. (2025) 'Optimal subsampling proportional subdistribution hazards regression with rare events in big data', Statistics and its Interface, 18 (3), pp. 361 - 377. doi: 10.4310/SII.250118022821. | en_US |
dc.identifier.issn | 1938-7989 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28519 | - |
dc.description | The data set is provided by Surveillance Research Program, National Cancer Institute SEER*Stat software (seer.cancer.gov/seerstat) version 8.3.9.1. | en_US |
dc.description | 2000 Mathematics Subject Classification: Primary 62N01; Secondary 62P10. | - |
dc.description.abstract | The proportional subdistribution hazards (PSH) model has been widely employed for analyzing competing risks data which have mutually exclusive events with multiple causes and commonly occur in clinical research. With the rapid development of healthcare industry, massively sized survival data sets are becoming increasingly prevalent and classical PSH models are computationally intensive with large data sets. In this article, we propose the optimal subsampling estimators and two-step algorithm for the Fine-Gray model. Asymptotic properties of the proposed estimators are established and an extensive simulation study is conducted to demonstrate the efficiency of the estimators. Our proposed methodology is then illustrated with the large dataset from the SEER (Surveillance, Epidemiology, and End Results) database. | en_US |
dc.description.sponsorship | National Natural Science Funds of China (Grant No. 12101015); Scientific Research Foundation of North China University of Technology (No. 110051360002); Fundamental Research Funds for Beijing Universities, NCUT (No.110052971921/007); National Natural Science Foundation of China (No.11861042); China Statistical Research Project (No. 2020LZ25). | en_US |
dc.format.extent | 361 - 377 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | International Press | en_US |
dc.rights | Copyright © 2025 International Press of Boston, Inc. All Rights Reserved. A copy of the published Work may be posted to an institutional repository or archive, whose content is accessible solely to users within the institution, at an institution with whom the Author was affiliated at the time of the Work’s publication by the Publisher (see: https://www.intlpress.com/site/pub/files/journal_author_ctp_form/author_consent_to_publish_form_cms.pdf). | - |
dc.rights.uri | https://www.intlpress.com/site/pub/files/journal_author_ctp_form/author_consent_to_publish_form_cms.pdf | - |
dc.subject | big data | en_US |
dc.subject | competing risks data | en_US |
dc.subject | optimal subsampling | en_US |
dc.title | Optimal subsampling proportional subdistribution hazards regression with rare events in big data | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.4310/SII.250118022821 | - |
dc.relation.isPartOf | Statistics and its Interface | - |
pubs.issue | 3 | - |
pubs.publication-status | Published | - |
pubs.volume | 18 | - |
dc.identifier.eissn | 1938-7997 | - |
dcterms.dateAccepted | 2024-03-05 | - |
dc.rights.holder | International Press of Boston, Inc. | - |
Appears in Collections: | Dept of Mathematics Research Papers |
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FullText.pdf | Copyright © 2025 International Press of Boston, Inc. All Rights Reserved. A copy of the published Work may be posted to an institutional repository or archive, whose content is accessible solely to users within the institution, at an institution with whom the Author was affiliated at the time of the Work’s publication by the Publisher (see: https://www.intlpress.com/site/pub/files/journal_author_ctp_form/author_consent_to_publish_form_cms.pdf). | 634.37 kB | Adobe PDF | View/Open |
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