The ‘So What’ Factor: Statistical versus Cinical Significance

Stapleton, Claire; Scott, Mark; Atkinson, Greg

doi:10.1055/s-0029-1241216

Cited by 9 publications

(8 citation statements)

References 6 publications

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“…Similarly, although a p-value of 0.05 is almost overwhelmingly taken as the ‘significance level’, many statisticians strongly advise against its unconsidered use and suggest that other levels (such as 0.01 or 0.10) may be more appropriate for certain problems and desired inferences. A number of commentators in the sports science field have made similar observations [ 1 , 37 , 38 ]. The overwhelming advice is that the probabilities obtained as a result of statistical analysis must be useful in providing decision support for the problem at hand, and different probabilities can indeed be used if they are well justified, transparently reported and correctly interpreted.…”

Section: Discussionmentioning

confidence: 73%

Bayesian Estimation of Small Effects in Exercise and Sports Science

et al. 2016

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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.

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Section: Discussionmentioning

confidence: 73%

Bayesian Estimation of Small Effects in Exercise and Sports Science

et al. 2016

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“…Patients with a shorter duration of pain showed a trend towards statistically better outcomes, although the magnitude of the differences between the groups were, in most instances, below the MCID. Stapleton et al 21 highlighted the importance of clinically significant results, rather than interpreting a p-value in isolation.…”

Section: Discussionmentioning

confidence: 99%

Does Duration of Pain at Baseline Influence Clinical Outcomes of Low Back Pain Patients Managed on an Evidence-based Pathway?

Jess

Ryan

Hamilton

et al. 2018

Spine

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“…Given the small number of studies, and substantial variation in experimental approaches, dependent measures and outcomes, more well-designed studies of probiotic supplementation in various athlete groups are warranted. These studies will clarify the issue of clinical/practical significance of reported benefits in addition to statements of statistical significance (Stapleton, Scott, & Atkinson, 2009).…”

Section: Studies On Probiotics In Athletesmentioning

confidence: 94%

Probiotics supplementation for athletes – Clinical and physiological effects

Pyne

West

Cox

et al. 2014

European Journal of Sport Science

103

119

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Probiotic supplementation has traditionally focused on gut health. However, in recent years, the clinical applications of probiotics have broadened to allergic, metabolic, inflammatory, gastrointestinal and respiratory conditions. Gastrointestinal health is important for regulating adaptation to exercise and physical activity. Symptoms such as nausea, bloating, cramping, pain, diarrhoea and bleeding occur in some athletes, particularly during prolonged exhaustive events. Several studies conducted since 2006 examining probiotic supplementation in athletes or highly active individuals indicate modest clinical benefits in terms of reduced frequency, severity and/or duration of respiratory and gastrointestinal illness. The likely mechanisms of action for probiotics include direct interaction with the gut microbiota, interaction with the mucosal immune system and immune signalling to a variety of organs and systems. Practical issues to consider include medical and dietary screening of athletes, sourcing of recommended probiotics and formulations, dose-response requirements for different probiotic strains, storage, handling and transport of supplements and timing of supplementation in relation to travel and competition.

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The ‘So What’ Factor: Statistical versus Cinical Significance

Cited by 9 publications

References 6 publications

Bayesian Estimation of Small Effects in Exercise and Sports Science

Bayesian Estimation of Small Effects in Exercise and Sports Science

Does Duration of Pain at Baseline Influence Clinical Outcomes of Low Back Pain Patients Managed on an Evidence-based Pathway?

Probiotics supplementation for athletes – Clinical and physiological effects

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