  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.
