Comparison of normal distribution-based and nonparametric decision limits on the GH-2000 score for detecting growth hormone misuse (doping) in sport

This paper is motivated by the GH-2000 biomarker test, though the discussion is applicable to other diagnostic tests. The GH-2000 biomarker test has been developed as a powerful technique to detect growth hormone misuse by athletes, based on the GH-2000 score. Decision limits on the GH-2000 score have been developed and incorporated into the guidelines of the World Anti-Doping Agency (WADA). These decision limits are constructed, however, under the assumption that the GH-2000 score follows a normal distribution. As it is difficult to affirm the normality of a distribution based on a finite sample, nonparametric decision limits, readily available in the statistical literature, are viable alternatives. In this paper, we compare the normal distribution-based and nonparametric decision limits. We show that the decision limit based on the normal distribution may deviate significantly from the nominal confidence level 1 - α or nominal FPR γ when the distribution of the GH-2000 score departs only slightly from the normal distribution. While a nonparametric decision limit does not assume any specific distribution of the GH-2000 score and always guarantees the nominal confidence level and FPR, it requires a much larger sample size than the normal distribution-based decision limit. Due to the stringent FPR of the GH-2000 biomarker test used by WADA, the sample sizes currently available are much too small, and it will take many years of testing to have the minimum sample size required, in order to use the nonparametric decision limits. Large sample theory about the normal distribution-based and nonparametric decision limits is also developed in this paper to help understanding their behaviours when the sample size is large.

A correction to the age-adjustment of the GH-2000 score used in the detection of growth hormone misuse

OBJECTIVE

The GH-2000 biomarker test has been introduced by the World Anti-Doping Agency as a method of detecting growth hormone misuse in professional sport. The test involves the measurement insulin-like growth factor-I and the amino-terminal pro-peptide of type III collagen (P-III-NP) which increase in a dose-dependent manner in response to GH. These measurements are combined in sex specific formulae that include an age adjustment. The original age adjustment overcorrects the effect of age in male athletes and could potentially place older men at a disadvantage. The purpose of this note is to investigate the performance of a previously suggested correction term in two new and larger data sets.

RESULTS

The GH-2000 score was calculated for 7307 samples obtained from 15 accredited WADA laboratories in 2017 and 3916 samples measured at Drug Control Centre, King's College London, UK between 2013 and 2017. The GH-2000 scores were investigated for positive age effects using standard regression modelling. As previously, all analyses confirmed a positive age effect. Applying the earlier suggested correction term of 0.032 × age showed a significant over-correction leading to a negative association of the GH-2000 score with age. We now suggest a smaller age correction of 0.020 × age, which corresponds to the smallest effect found in the earlier studies.

Statistical methodology for age-adjustment of the GH-2000 score detecting growth hormone misuse

Background

The GH-2000 score has been developed as a powerful and unique technique for the detection of growth hormone misuse by sportsmen and women. The score depends upon the measurement of two growth hormone (GH) sensitive markers, insulin-like growth factor-I (IGF-I) and the amino-terminal pro-peptide of type III collagen (P-III-NP). With the collection and establishment of an increasingly large database it has become apparent that the score shows a positive age effect in the male athlete population, which could potentially place older male athletes at a disadvantage.

Methods

We have used results from residual analysis of the general linear model to show that the residual of the GH-2000 score when regressed on the mean-age centred age is an appropriate way to proceed to correct this bias. As six GH-2000 scores are possible depending on the assays used for determining IGF-I and P-III-NP, methodology had to be explored for including six different age effects into a unique residual. Meta-analytic techniques have been utilized to find a summary age effect.

Results

The age-adjusted GH-2000 score, a form of residual, has similar mean and variance as the original GH-2000 score and, hence, the developed decision limits show negligible change when compared to the decision limits based on the original score. We also show that any further scale-transformation will not change the adjusted score. Hence the suggested adjustment is optimal for the given data. The summary age effect is homogeneous across the six scores, and so the generic adjustment of the GH-2000 score formula is justified.

Conclusions

A final revised GH-2000 score formula is provided which is independent of the age of the athlete under consideration.