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Biostatistics Advance Access published online on June 19, 2006
Biostatistics, doi:10.1093/biostatistics/kxl009
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Received March 24, 2006
Revised May 24, 2006
Accepted June 8, 2006

Article

Bayesian detection of abnormal values in longitudinal biomarkers with an application to T/E ratio

Pierre-Edouard Sottas 1 *, Norbert Baume 1, Christophe Saudan 1, Carine Schweizer 1, Matthias Kamber 2, and Martial Saugy 1

1 Swiss Laboratory for Doping Analyses, Université de Lausanne, 1066 Lausanne, Switzerland
2 Swiss Federal Office for Sports BASPO, 2532 Macolin, Switzerland

* To whom correspondence should be addressed.
Pierre-Edouard Sottas, E-mail: Pierre-Edouard.Sottas{at}hospvd.ch


  Abstract

We developed a test that compares sequential measurements of a biomarker against previous readings performed on the same individual. A probability mass function expresses prior information on inter-individual variations of intra-individual parameters. Then, the model progressively integrates new readings to more accurately quantify the characteristics of the individual. This Bayesian framework generalizes the two main approaches currently used in forensic toxicology for the detection of abnormal values of a biomarker. The specificity is independent of the number n of previous test results, with a model that gradually evolves from population-derived limits when n=0 to individual-based cut-off thresholds when n is large.

We applied this model to detect abnormal values in an athlete's steroid profile characterized by the testosterone over epitestosterone (T/E) marker. A cross-validation procedure was used for the estimation of prior densities as well as model validation. The heightened sensitivity/specificity relation obtained on a large dataset shows that longitudinal monitoring of an athlete's steroid profile may be used efficiently to detect the abuse of testosterone and its precursors in sports. Mild assumptions make the model interesting for other areas of forensic toxicology.


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