Eleftheriou Dimitra, Piper Thomas, Thevis Mario, Neocleous Tereza
Leiden Academic Centre for Drug Research, 4496 Leiden University , Leiden, The Netherlands.
Center for Preventive Doping Research - Institute of Biochemistry, German Sport University Cologne, Cologne, Germany.
Int J Biostat. 2025 Mar 28;21(1):165-181. doi: 10.1515/ijb-2024-0019. eCollection 2025 May 1.
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.
对运动员尿液类固醇谱进行生物标志物分析对于反兴奋剂工作的成功至关重要。当前的统计分析方法基于尿液类固醇谱纵向生物标志物值的单变量建模为每位运动员生成个性化限值。然而，对多种生物标志物进行同时建模有可能进一步提高异常检测能力。在本研究中，我们提出了一种用于纵向数据分析的多变量贝叶斯自适应模型，该模型扩展了法医毒理学中已有的单生物标志物模型。所提出的方法采用马尔可夫链蒙特卡罗抽样方法，并通过一类分类算法解决已确认异常值稀缺的问题。通过在获得新测量值时调整决策边界，该模型为每位运动员提供了稳健且个性化的检测阈值。我们在一个包含229名运动员的数据库上测试了所提出的方法，该数据库包括含有分类为正常、非典型或已确认异常样本的纵向类固醇谱。我们的结果表明检测性能有所提高，突出了多变量方法在兴奋剂检测中的潜在价值。
