Näytä suppeat kuvailutiedot

dc.contributor.authorReinikainen, Jaakko
dc.contributor.authorKarvanen, Juha
dc.date.accessioned2022-10-06T10:34:04Z
dc.date.available2022-10-06T10:34:04Z
dc.date.issued2022
dc.identifier.citationReinikainen, J., & Karvanen, J. (2022). Bayesian subcohort selection for longitudinal covariate measurements in follow‐up studies. <i>Statistica Neerlandica</i>, <i>76</i>(4), 372-390. <a href="https://doi.org/10.1111/stan.12264" target="_blank">https://doi.org/10.1111/stan.12264</a>
dc.identifier.otherCONVID_104028793
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/83480
dc.description.abstractWe propose an approach for the planning of longitudinal covariate measurements in follow-up studies where covariates are time-varying. We assume that the entire cohort cannot be selected for longitudinal measurements due to financial limitations, and study how a subset of the cohort should be selected optimally, in order to obtain precise estimates of covariate effects in a survival model. In our approach, the study will be designed sequentially utilizing the data collected in previous measurements of the individuals as prior information. We propose using a Bayesian optimality criterion in the subcohort selections, which is compared with simple random sampling using simulated and real follow-up data. Our work improves the computational approach compared to the previous research on the topic so that designs with several covariates and measurement points can be implemented. As an example we derive the optimal design for studying the effect of body mass index and smoking on all-cause mortality in a Finnish longitudinal study. Our results support the conclusion that the precision of the estimates can be clearly improved by optimal design.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherWiley-Blackwell
dc.relation.ispartofseriesStatistica Neerlandica
dc.rightsCC BY 4.0
dc.subject.otherBayesian optimal design
dc.subject.otherdata collection
dc.subject.otherfollow-up study
dc.subject.otherlongitudinal measurements
dc.subject.otherstudy design
dc.titleBayesian subcohort selection for longitudinal covariate measurements in follow‐up studies
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202210064810
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineStatisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange372-390
dc.relation.issn0039-0402
dc.relation.numberinseries4
dc.relation.volume76
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 The Authors. Statistica Neerlandica published by John Wiley & Sons Ltd on behalf of Netherlands Society for Statistics and Operations Research.
dc.rights.accesslevelopenAccessfi
dc.subject.ysopitkittäistutkimus
dc.subject.ysobayesilainen menetelmä
dc.subject.ysoseurantatutkimus
dc.subject.ysokohorttitutkimus
dc.subject.ysootanta
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p14610
jyx.subject.urihttp://www.yso.fi/onto/yso/p17803
jyx.subject.urihttp://www.yso.fi/onto/yso/p13719
jyx.subject.urihttp://www.yso.fi/onto/yso/p25606
jyx.subject.urihttp://www.yso.fi/onto/yso/p12939
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1111/stan.12264
jyx.fundinginformationEmil Aaltonen Foundation
dc.type.okmA1


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