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dc.contributor.authorMengersen, Kerrie L
dc.contributor.authorDrovandi, Christopher C
dc.contributor.authorRobert, Christian P
dc.contributor.authorPyne, David B
dc.contributor.authorGore, Christopher J
dc.date.accessioned2020-02-20T02:54:28Z
dc.date.available2020-02-20T02:54:28Z
dc.date.issued2016
dc.identifier.issn1932-6203en_US
dc.identifier.doi10.1371/journal.pone.0147311en_US
dc.identifier.urihttp://hdl.handle.net/10072/391707
dc.description.abstractThe 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_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.language.isoeng
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofissue4en_US
dc.relation.ispartofjournalPLoS Oneen_US
dc.relation.ispartofvolume11en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsMultidisciplinary Sciencesen_US
dc.subject.keywordsScience & Technology - Other Topicsen_US
dc.subject.keywordsHEMOGLOBIN MASSen_US
dc.subject.keywordsRUNNING ECONOMYen_US
dc.titleBayesian Estimation of Small Effects in Exercise and Sports Scienceen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationMengersen, KL; Drovandi, CC; Robert, CP; Pyne, DB; Gore, CJ, Bayesian Estimation of Small Effects in Exercise and Sports Science, PLoS One, 2016, 11 (4)en_US
dcterms.dateAccepted2015-12-31
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/en_US
dc.date.updated2020-02-20T02:52:41Z
dc.description.versionVersion of Record (VoR)en_US
gro.rights.copyright© 2016 Mengersen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
gro.hasfulltextFull Text
gro.griffith.authorPyne, David B.


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