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dc.contributor.authorReinikainen, Jaakko
dc.contributor.authorKarvanen, Juha
dc.contributor.authorTolonen, Hanna
dc.date.accessioned2016-01-21T09:04:43Z
dc.date.available2016-01-21T09:04:43Z
dc.date.issued2016
dc.identifier.citationReinikainen, J., Karvanen, J., & Tolonen, H. (2016). How many longitudinal covariate measurements are needed for risk prediction?. <em>Journal of Clinical Epidemiology</em>, 69, 114-124. <a href="http://dx.doi.org/10.1016/j.jclinepi.2015.06.022">doi:10.1016/j.jclinepi.2015.06.022</a>
dc.identifier.otherTUTKAID_66840
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/48394
dc.description.abstractObjective: In epidemiological follow-up studies, many key covariates, such as smoking, use of medication, blood pressure and cholesterol, are time-varying. Because of practical and financial limitations, time-varying covariates cannot be measured continuously, but only at certain prespecified time points. We study how the number of these longitudinal measurements can be chosen cost-efficiently by evaluating the usefulness of the measurements for risk prediction. Study Design and Setting: The usefulness is addressed by measuring the improvement in model discrimination between models using different amounts of longitudinal information. We use simulated follow-up data and the data from the Finnish East–West study, a follow-up study, with eight longitudinal covariate measurements carried out between 1959 and 1999. Results: In a simulation study, we show how the variability and the hazard ratio of a time-varying covariate are connected to the importance of re-measurements. In the East–West study, it is seen that for older people, the risk predictions obtained using only every other measurement are almost equivalent to the predictions obtained using all eight measurements. Conclusion: Decisions about the study design have significant effects on the costs. The cost-efficiency can be improved by applying the measures of model discrimination to data from previous studies and simulations.
dc.language.isoeng
dc.publisherElsevier Inc.
dc.relation.ispartofseriesJournal of Clinical Epidemiology
dc.subject.otherlongitudinal measurements
dc.subject.othermodel discrimination
dc.subject.otherrisk prediction
dc.subject.otherstudy design
dc.titleHow many longitudinal covariate measurements are needed for risk prediction?
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201601151125
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.oppiaineTilastotiede
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2016-01-15T13:15:12Z
dc.type.coarjournal article
dc.description.reviewstatuspeerReviewed
dc.format.pagerange114-124
dc.relation.issn0895-4356
dc.relation.volume69
dc.type.versionsubmittedVersion
dc.rights.copyright© 2016 Elsevier Inc. This is a preprint version of an article whose final and definitive form has been published by Elsevier.
dc.rights.accesslevelopenAccessfi
dc.relation.doi10.1016/j.jclinepi.2015.06.022


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