A multivariate Bayesian learning approach for improved detection of doping in athletes using urinary steroid profiles

Eleftheriou Dimitra (), Piper Thomas, Thevis Mario and Neocleous Tereza
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Eleftheriou Dimitra: Leiden Academic Centre for Drug Research, 4496 Leiden University , Leiden, The Netherlands
Piper Thomas: Center for Preventive Doping Research – Institute of Biochemistry, German Sport University Cologne, Cologne, Germany
Thevis Mario: Center for Preventive Doping Research – Institute of Biochemistry, German Sport University Cologne, Cologne, Germany
Neocleous Tereza: School of Mathematics and Statistics, University of Glasgow, Glasgow, UK

The International Journal of Biostatistics, 2025, vol. 21, issue 1, 165-181

Abstract: Biomarker analysis of athletes’ urinary steroid profiles is crucial for the success of anti-doping efforts. Current statistical analysis methods generate personalised limits for each athlete based on univariate modelling of longitudinal biomarker values from the urinary steroid profile. However, simultaneous modelling of multiple biomarkers has the potential to further enhance abnormality detection. In this study, we propose a multivariate Bayesian adaptive model for longitudinal data analysis, which extends the established single-biomarker model in forensic toxicology. The proposed approach employs Markov chain Monte Carlo sampling methods and addresses the scarcity of confirmed abnormal values through a one-class classification algorithm. By adapting decision boundaries as new measurements are obtained, the model provides robust and personalised detection thresholds for each athlete. We tested the proposed approach on a database of 229 athletes, which includes longitudinal steroid profiles containing samples classified as normal, atypical, or confirmed abnormal. Our results demonstrate improved detection performance, highlighting the potential value of a multivariate approach in doping detection.

Keywords: anti-doping; Bayesian adaptive model; longitudinal biomarker data; multivariate analysis; one-class classification; urinary steroid profile (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/ijb-2024-0019

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