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Article: A quick and accurate method for the estimation of covariate effects based on empirical Bayes estimates in mixed-effects modeling: Correction of bias due to shrinkage

TitleA quick and accurate method for the estimation of covariate effects based on empirical Bayes estimates in mixed-effects modeling: Correction of bias due to shrinkage
Authors
KeywordsCovariate analysis
Empirical Bayes estimates
Nonlinear mixed-effects model
Population analysis
Shrinkage
Issue Date2018
PublisherSage Publications Ltd. The Journal's web site is located at http://smm.sagepub.com
Citation
Statistical Methods in Medical Research, 2018 How to Cite?
AbstractNonlinear mixed-effects modeling is a popular approach to describe the temporal trajectory of repeated measurements of clinical endpoints collected over time in clinical trials, to distinguish the within-subject and the between-subject variabilities, and to investigate clinically important risk factors (covariates) that may partly explain the between-subject variability. Due to the complex computing algorithms involved in nonlinear mixed-effects modeling, estimation of covariate effects is often time-consuming and error-prone owing to local convergence. We develop a fast and accurate estimation method based on empirical Bayes estimates from the base mixed-effects model without covariates, and simple regressions outside of the nonlinear mixed-effect modeling framework. Application of the method is illustrated using a pharmacokinetic dataset from an anticoagulation drug for the prevention of major cardiovascular events in patients with acute coronary syndrome. Both the application and extensive simulations demonstrated that the performance of this high-throughput method is comparable to the commonly used maximum likelihood estimation in nonlinear mixed-effects modeling.
Persistent Identifierhttp://hdl.handle.net/10722/272346
ISSN
2017 Impact Factor: 2.284
2015 SCImago Journal Rankings: 3.774

 

DC FieldValueLanguage
dc.contributor.authorYuan, M-
dc.contributor.authorXu, XS-
dc.contributor.authorYang, Y-
dc.contributor.authorXu, J-
dc.contributor.authorHuang, X-
dc.contributor.authorTao, F-
dc.contributor.authorZhao, L-
dc.contributor.authorZhang, L-
dc.contributor.authorPinheiro, J-
dc.date.accessioned2019-07-20T10:40:31Z-
dc.date.available2019-07-20T10:40:31Z-
dc.date.issued2018-
dc.identifier.citationStatistical Methods in Medical Research, 2018-
dc.identifier.issn0962-2802-
dc.identifier.urihttp://hdl.handle.net/10722/272346-
dc.description.abstractNonlinear mixed-effects modeling is a popular approach to describe the temporal trajectory of repeated measurements of clinical endpoints collected over time in clinical trials, to distinguish the within-subject and the between-subject variabilities, and to investigate clinically important risk factors (covariates) that may partly explain the between-subject variability. Due to the complex computing algorithms involved in nonlinear mixed-effects modeling, estimation of covariate effects is often time-consuming and error-prone owing to local convergence. We develop a fast and accurate estimation method based on empirical Bayes estimates from the base mixed-effects model without covariates, and simple regressions outside of the nonlinear mixed-effect modeling framework. Application of the method is illustrated using a pharmacokinetic dataset from an anticoagulation drug for the prevention of major cardiovascular events in patients with acute coronary syndrome. Both the application and extensive simulations demonstrated that the performance of this high-throughput method is comparable to the commonly used maximum likelihood estimation in nonlinear mixed-effects modeling.-
dc.languageeng-
dc.publisherSage Publications Ltd. The Journal's web site is located at http://smm.sagepub.com-
dc.relation.ispartofStatistical Methods in Medical Research-
dc.rightsCopyright © 2018 The Author(s). DOI: 10.1177/0962280218812595-
dc.subjectCovariate analysis-
dc.subjectEmpirical Bayes estimates-
dc.subjectNonlinear mixed-effects model-
dc.subjectPopulation analysis-
dc.subjectShrinkage-
dc.titleA quick and accurate method for the estimation of covariate effects based on empirical Bayes estimates in mixed-effects modeling: Correction of bias due to shrinkage-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1177/0962280218812595-
dc.identifier.pmid30409080-
dc.identifier.scopuseid_2-s2.0-85060353834-
dc.identifier.hkuros299463-
dc.publisher.placeUnited Kingdom-

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