Competitive performance as a discriminator of doping status in elite athletes.

Autor: Hopker JG; School of Sport & Exercise Sciences, University of Kent, Canterbury, Kent, UK., Griffin JE; Department of Statistical Science, University College London, London, UK., Hinoveanu LC; School of Sport & Exercise Sciences, University of Kent, Canterbury, Kent, UK., Saugy J; Research & Expertise in Antidoping Sciences, University of Lausanne, Lausanne, Switzerland., Faiss R; Research & Expertise in Antidoping Sciences, University of Lausanne, Lausanne, Switzerland.
Jazyk: angličtina
Zdroj: Drug testing and analysis [Drug Test Anal] 2024 May; Vol. 16 (5), pp. 473-481. Date of Electronic Publication: 2023 Aug 21.
DOI: 10.1002/dta.3563
Abstrakt: As the aim of any doping regime is to improve sporting performance, it has been suggested that analysis of athlete competitive results might be informative in identifying those at greater risk of doping. This research study aimed to investigate the utility of a statistical performance model to discriminate between athletes who have a previous anti-doping rule violation (ADRV) and those who do not. We analysed performances of male and female 100 and 800 m runners obtained from the World Athletics database using a Bayesian spline model. Measures of unusual improvement in performance were quantified by comparing the yearly change in athlete's performance (delta excess performance) to quantiles of performance in their age-matched peers from the database population. The discriminative ability of these measures was investigated using the area under the ROC curve (AUC) with the 55%, 75% and 90% quantiles of the population performance. The highest AUC values across age were identified for the model with a 75% quantile (AUC = 0.78-0.80). The results of this study demonstrate that delta excess performance was able to discriminate between athletes with and without ADRVs and therefore could be used to assist in the risk stratification of athletes for anti-doping purposes.
(© 2023 The Authors. Drug Testing and Analysis published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
Externí odkaz: