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dc.contributor.authorMengersen, Kerrie*
dc.contributor.authorDrovandi, Christopher C.*
dc.contributor.authorRobert, Christian P.*
dc.contributor.authorPyne, David B.*
dc.contributor.authorGore, Christopher J.*
dc.date.accessioned2017-03-09T16:18:22Z
dc.date.available2017-03-09T16:18:22Z
dc.date.issued2016
dc.identifier.issn1932-6203
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16323
dc.language.isoenen
dc.subjectBayesian analysisen
dc.subject.ddc519en
dc.titleBayesian Estimation of Small Effects in Exercise and Sports Scienceen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThe aim of this paper is to provide a Bayesian formulation of the so-called magnitude-based inference approach to quantifying and interpreting effects, and in a case study example provide accurate probabilistic statements that correspond to the intended magnitude-based inferences. The model is described in the context of a published small-scale athlete study which employed a magnitude-based inference approach to compare the effect of two altitude training regimens (live high-train low (LHTL), and intermittent hypoxic exposure (IHE)) on running performance and blood measurements of elite triathletes. The posterior distributions, and corresponding point and interval estimates, for the parameters and associated effects and comparisons of interest, were estimated using Markov chain Monte Carlo simulations. The Bayesian analysis was shown to provide more direct probabilistic comparisons of treatments and able to identify small effects of interest. The approach avoided asymptotic assumptions and overcame issues such as multiple testing. Bayesian analysis of unscaled effects showed a probability of 0.96 that LHTL yields a substantially greater increase in hemoglobin mass than IHE, a 0.93 probability of a substantially greater improvement in running economy and a greater than 0.96 probability that both IHE and LHTL yield a substantially greater improvement in maximum blood lactate concentration compared to a Placebo. The conclusions are consistent with those obtained using a ‘magnitude-based inference’ approach that has been promoted in the field. The paper demonstrates that a fully Bayesian analysis is a simple and effective way of analysing small effects, providing a rich set of results that are straightforward to interpret in terms of probabilistic statements.en
dc.relation.isversionofjnlnamePLoS ONE
dc.relation.isversionofjnlvol11en
dc.relation.isversionofjnlissue4en
dc.relation.isversionofjnldate2016
dc.relation.isversionofjnlpagese0147311en
dc.relation.isversionofdoi10.1371/journal.pone.0147311en
dc.identifier.urlsitehttp://dx.doi.org/10.1371/journal.pone.0147311en
dc.relation.isversionofjnlpublisherPublic Library of Scienceen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2017-03-07T17:33:41Z
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