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Article: A semiparametric joint model for cluster size and subunit‐specific interval‐censored outcomes

TitleA semiparametric joint model for cluster size and subunit‐specific interval‐censored outcomes
Authors
Keywordsdental study
EM algorithm
informative cluster size
random effect model
sieve estimation
Issue Date15-Dec-2022
PublisherWiley
Citation
Biometrics, 2022 How to Cite?
Abstract

Clustered data frequently arise in biomedical studies, where observations, or subunits, measured within a cluster are associated. The cluster size is said to be informative, if the outcome variable is associated with the number of subunits in a cluster. In most existing work, the informative cluster size issue is handled by marginal approaches based on within-cluster resampling, or cluster-weighted generalized estimating equations. Although these approaches yield consistent estimation of the marginal models, they do not allow estimation of within-cluster associations and are generally inefficient. In this paper, we propose a semiparametric joint model for clustered interval-censored event time data with informative cluster size. We use a random effect to account for the association among event times of the same cluster as well as the association between event times and the cluster size. For estimation, we propose a sieve maximum likelihood approach and devise a computationally-efficient expectation-maximization algorithm for implementation. The estimators are shown to be strongly consistent, with the Euclidean components being asymptotically normal and achieving semiparametric efficiency. Extensive simulation studies are conducted to evaluate the finite-sample performance, efficiency and robustness of the proposed method. We also illustrate our method via application to a motivating periodontal disease dataset.


Persistent Identifierhttp://hdl.handle.net/10722/331600
ISSN
2023 Impact Factor: 1.4
2023 SCImago Journal Rankings: 1.480
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLee, Chun Yin-
dc.contributor.authorWong, Kin Yau-
dc.contributor.authorLam, Kwok Fai-
dc.contributor.authorBandyopadhyay, Dipankar-
dc.date.accessioned2023-09-21T06:57:17Z-
dc.date.available2023-09-21T06:57:17Z-
dc.date.issued2022-12-15-
dc.identifier.citationBiometrics, 2022-
dc.identifier.issn0006-341X-
dc.identifier.urihttp://hdl.handle.net/10722/331600-
dc.description.abstract<p>Clustered data frequently arise in biomedical studies, where observations, or subunits, measured within a cluster are associated. The cluster size is said to be informative, if the outcome variable is associated with the number of subunits in a cluster. In most existing work, the informative cluster size issue is handled by marginal approaches based on within-cluster resampling, or cluster-weighted generalized estimating equations. Although these approaches yield consistent estimation of the marginal models, they do not allow estimation of within-cluster associations and are generally inefficient. In this paper, we propose a semiparametric joint model for clustered interval-censored event time data with informative cluster size. We use a random effect to account for the association among event times of the same cluster as well as the association between event times and the cluster size. For estimation, we propose a sieve maximum likelihood approach and devise a computationally-efficient expectation-maximization algorithm for implementation. The estimators are shown to be strongly consistent, with the Euclidean components being asymptotically normal and achieving semiparametric efficiency. Extensive simulation studies are conducted to evaluate the finite-sample performance, efficiency and robustness of the proposed method. We also illustrate our method via application to a motivating periodontal disease dataset.<br></p>-
dc.languageeng-
dc.publisherWiley-
dc.relation.ispartofBiometrics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectdental study-
dc.subjectEM algorithm-
dc.subjectinformative cluster size-
dc.subjectrandom effect model-
dc.subjectsieve estimation-
dc.titleA semiparametric joint model for cluster size and subunit‐specific interval‐censored outcomes-
dc.typeArticle-
dc.identifier.doi10.1111/biom.13795-
dc.identifier.scopuseid_2-s2.0-85144150304-
dc.identifier.eissn1541-0420-
dc.identifier.isiWOS:000898397200001-
dc.identifier.issnl0006-341X-

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