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- Publisher Website: 10.1002/sim.8910
- Scopus: eid_2-s2.0-85101439753
- PMID: 33586218
- WOS: WOS:000617884600001
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Article: Marginal analysis of current status data with informative cluster size using a class of semiparametric transformation cure models
Title | Marginal analysis of current status data with informative cluster size using a class of semiparametric transformation cure models |
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Authors | |
Keywords | cure model current status data estimating equations informative cluster size survival analysis |
Issue Date | 2021 |
Publisher | John Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/ |
Citation | Statistics in Medicine, 2021, v. 40 n. 10, p. 2400-2412 How to Cite? |
Abstract | This 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. |
Persistent Identifier | http://hdl.handle.net/10722/302093 |
ISSN | 2023 Impact Factor: 1.8 2023 SCImago Journal Rankings: 1.348 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Lam, KF | - |
dc.contributor.author | LEE, CY | - |
dc.contributor.author | Wong, KY | - |
dc.contributor.author | Bandyopadhyah, D | - |
dc.date.accessioned | 2021-08-21T03:31:28Z | - |
dc.date.available | 2021-08-21T03:31:28Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Statistics in Medicine, 2021, v. 40 n. 10, p. 2400-2412 | - |
dc.identifier.issn | 0277-6715 | - |
dc.identifier.uri | http://hdl.handle.net/10722/302093 | - |
dc.description.abstract | This 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. | - |
dc.language | eng | - |
dc.publisher | John Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/ | - |
dc.relation.ispartof | Statistics in Medicine | - |
dc.rights | Submitted (preprint) Version This is the pre-peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Accepted (peer-reviewed) Version This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. | - |
dc.subject | cure model | - |
dc.subject | current status data | - |
dc.subject | estimating equations | - |
dc.subject | informative cluster size | - |
dc.subject | survival analysis | - |
dc.title | Marginal analysis of current status data with informative cluster size using a class of semiparametric transformation cure models | - |
dc.type | Article | - |
dc.identifier.email | Lam, KF: hrntlkf@hkucc.hku.hk | - |
dc.identifier.authority | Lam, KF=rp00718 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1002/sim.8910 | - |
dc.identifier.pmid | 33586218 | - |
dc.identifier.scopus | eid_2-s2.0-85101439753 | - |
dc.identifier.hkuros | 324216 | - |
dc.identifier.volume | 40 | - |
dc.identifier.issue | 10 | - |
dc.identifier.spage | 2400 | - |
dc.identifier.epage | 2412 | - |
dc.identifier.isi | WOS:000617884600001 | - |
dc.publisher.place | United Kingdom | - |