Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/90540
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dc.contributorDepartment of Applied Mathematicsen_US
dc.creatorLam, KFen_US
dc.creatorLee, CYen_US
dc.creatorWong, KYen_US
dc.creatorBandyopadhyay, Den_US
dc.date.accessioned2021-07-22T05:35:15Z-
dc.date.available2021-07-22T05:35:15Z-
dc.identifier.issn0277-6715en_US
dc.identifier.urihttp://hdl.handle.net/10397/90540-
dc.language.isoenen_US
dc.publisherJohn Wiley & Sonsen_US
dc.rights©2021 John Wiley & Sons, Ltd.en_US
dc.rightsThis is the peer reviewed version of the following article: Lam, KF, Lee, CY, Wong, KY, Bandyopadhyay, D. Marginal analysis of current status data with informative cluster size using a class of semiparametric transformation cure models. Statistics in Medicine. 2021; 40: 2400– 2412, which has been published in final form at https://doi.org/10.1002/sim.8910. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.en_US
dc.subjectCure modelen_US
dc.subjectCurrent status dataen_US
dc.subjectEstimating equationsen_US
dc.subjectInformative cluster sizeen_US
dc.subjectSurvival analysisen_US
dc.titleMarginal analysis of current status data with informative cluster size using a class of semiparametric transformation cure modelsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage2400en_US
dc.identifier.epage2412en_US
dc.identifier.volume40en_US
dc.identifier.issue10en_US
dc.identifier.doi10.1002/sim.8910en_US
dcterms.abstractThis research is motivated by a periodontal disease dataset that possesses certain special features. The dataset consists of clustered current status time-to-event observations with large and varying cluster sizes, where the cluster size is associated with the disease outcome. Also, heavy censoring is present in the data even with long follow-up time, suggesting the presence of a cured subpopulation. In this paper, we propose a computationally efficient marginal approach, namely the cluster-weighted generalized estimating equation approach, to analyze the data based on a class of semiparametric transformation cure models. The parametric and nonparametric components of the model are estimated using a Bernstein-polynomial based sieve maximum pseudo-likelihood approach. The asymptotic properties of the proposed estimators are studied. Simulation studies are conducted to evaluate the performance of the proposed estimators in scenarios with different degree of informative clustering and within-cluster dependence. The proposed method is applied to the motivating periodontal disease data for illustration.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationStatistics in medicine, 10 May 2021, v. 40, no. 10, p. 2400-2412en_US
dcterms.isPartOfStatistics in medicineen_US
dcterms.issued2021-05-10-
dc.identifier.scopus2-s2.0-85101439753-
dc.identifier.pmid33586218-
dc.identifier.eissn1097-0258en_US
dc.description.validate202107 bcvcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0979-n02-
dc.identifier.SubFormID2256-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
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