  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 classified as normal, atypical, or
confirmed abnormal. Our results demonstrate improved detection performance,
highlighting the potential value of a multivariate approach in doping
detection.
