A Multivariate Copula-based Bayesian Framework for Doping Detection

TL;DR

This paper introduces a multivariate Bayesian framework using copula models to improve doping detection by analyzing multiple biomarkers simultaneously, enhancing the Athlete Biological Passport's effectiveness.

Contribution

It extends the existing univariate ADAPTIVE method to a multivariate copula-based approach, capturing dependencies among biomarkers for more accurate doping detection.

Findings

Improved detection accuracy in simulations and real data

Effective modeling of biomarker dependencies

Enhanced multidimensional reference regions

Abstract

Doping control is an essential component of anti-doping organizations for protecting clean sports competitions. Since 2009, this mission has been complemented worldwide by the Athlete Biological Passport (ABP), used to monitor athletes' individual profiles over time. The practical implementation of the ABP is based on a Bayesian framework, called ADAPTIVE, intended to identify individual reference ranges outside of which an observation may indicate doping abuse. Currently, this method follows a univariate approach, relying on simultaneous univariate analysis of different markers. This work extends the ADAPTIVE method to a multivariate testing framework, making use of copula models to couple the marginal distribution of biomarkers with their dependency structure. After introducing the proposed copula-based hierarchical model, we discuss our approach to inference, grounded in a Bayesian spirit, and present an extension to multidimensional predictive reference regions. Focusing on the hematological module of the ABP, we evaluate the proposed framework in both data-driven simulations and real data.