title: Bayesian Estimation of Small Effects in Exercise and Sports Science.
abstract: The 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.
authors.affiliation: Science and Engineering Faculty, Mathematical Sciences, and Institute for Future Environments, Queensland University of Technology, Brisbane, Australia.|Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers in Big Data, Big Models and New Insights, Brisbane, Australia.
authors.forename: Kerrie L
authors.initials: KL
authors.lastname: Mengersen
authors.affiliation: Science and Engineering Faculty, Mathematical Sciences, and Institute for Future Environments, Queensland University of Technology, Brisbane, Australia.|Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers in Big Data, Big Models and New Insights, Brisbane, Australia.
authors.forename: Christopher C
authors.initials: CC
authors.lastname: Drovandi
authors.affiliation: Ceremade, Universite Paris Dauphine, Paris, France.
authors.forename: Christian P
authors.initials: CP
authors.lastname: Robert
authors.affiliation: Australian Institute of Sport, Canberra, Australia.|Research Institute for Sport and Exercise, University of Canberra, Bruce, ACT, Australia.
authors.forename: David B
authors.initials: DB
authors.lastname: Pyne
authors.affiliation: Australian Institute of Sport, Canberra, Australia.|Research Institute for Sport and Exercise, University of Canberra, Bruce, ACT, Australia.|Exercise Physiology Laboratory, Flinders University of South Australia, Bedford Park, South Australia.
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Gore
mesh_terms: D001499:Bayes Theorem; D015444:Exercise / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D013177:Sports / Q000502:physiology*; D013178:Sports Medicine