Statistical Classification of Abnormal Blood Profiles in Athletes

Sottas Pierre-Edouard, Robinson Neil, Giraud Sylvain, Taroni Franco, Kamber Matthias, Mangin Patrice and Saugy Martial
Additional contact information
Sottas Pierre-Edouard: Swiss Laboratory for Doping Analyses, Université de Lausanne, 1005 Lausanne, Switzerland
Robinson Neil: Swiss Laboratory for Doping Analyses, Université de Lausanne, 1005 Lausanne, Switzerland
Giraud Sylvain: Swiss Laboratory for Doping Analyses, Université de Lausanne, 1005 Lausanne, Switzerland
Taroni Franco: School of Criminal Justice, Université de Lausanne, 1015 Lausanne, Switzerland
Kamber Matthias: Swiss Federal Office for Sports, 2532 Macolin, Switzerland
Mangin Patrice: Institute of Legal Medicine, Université de Lausanne, 1005 Lausanne, Switzerland
Saugy Martial: Swiss Laboratory for Doping Analyses, Université de Lausanne, 1005 Lausanne, Switzerland

The International Journal of Biostatistics, 2006, vol. 2, issue 1, 23

Abstract: Blood doping has been challenging the scientific community since the early 1970's, where it was demonstrated that blood transfusion significantly improves physical performance. Here, we present through 3 applications how statistical classification techniques can assist the implementation of effective tests to deter blood doping in elite sports. In particular, we developed a new indirect and universal test of blood doping, called Abnormal Blood Profile Score (ABPS), based on the statistical classification of indirect biomarkers of altered erythropoiesis. Up to 601 hematological profiles have been compiled in a reference database. Twenty-one of them were obtained from blood samples withdrawn from professional athletes convicted of blood doping by other direct tests. Discriminative training algorithms were used jointly with cross-validation techniques to map these labeled reference profiles to target outputs. The strict cross-validation procedure facilitates the adherence to medico-legal standards mandated by the World Anti Doping Agency (WADA). The test has a sensitivity to recombinant erythropoietin (rhEPO) abuse up to 3 times better than current generative models, independently whether the athlete is currently taking rhEPO or has stopped the treatment. The test is also sensitive to any form of blood transfusion, autologous transfusion included. We finally conclude why a probabilistic approach should be encouraged for the evaluation of evidence in anti-doping area of investigation.

Keywords: statistical classification; blood doping; biomarkers; cross-validation; forensic standards; scientific evidence (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.2202/1557-4679.1011

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