Combined statistical decision limits based on two GH-2000 scores for the detection of growth hormone misuse.

The growth hormone-2000 biomarker method, based on the measurements of insulin-like growth factor-I and the amino-terminal pro-peptide of type III collagen, has been developed as a powerful technique for the detection of growth hormone misuse by athletes. Insulin-like growth factor-I and amino-terminal pro-peptide of type III collagen are combined in gender-specific formulas to create the growth hormone-2000 score, which is used to determine whether growth hormone has been administered. To comply with World Anti-Doping Agency regulations, each analyte must be measured by two methods. Insulin-like growth factor-I and amino-terminal pro-peptide of type III collagen can be measured by a number of approved methods, each leading to its own growth hormone-2000 score. Single decision limits for each growth hormone-2000 score have been introduced and developed by Bassett, Erotokritou-Mulligan, Holt, Böhning and their co-authors in a series of papers. These have been incorporated into the guidelines of the World Anti-Doping Agency. A joint decision limit was constructed based on the sample correlation between the two growth hormone-2000 scores generated from an available sample to increase the sensitivity of the biomarker method. This paper takes this idea further into a fully developed statistical approach. It constructs combined decision limits when two growth hormone-2000 scores from different assay combinations are used to decide whether an athlete has been misusing growth hormone. The combined decision limits are directly related to tolerance regions and constructed using a Bayesian approach. It is also shown to have highly satisfactory frequentist properties. The new approach meets the required false-positive rate with a pre-specified level of certainty.

Estimation of Effect Heterogeneity in Rare Events Meta-Analysis.

The paper outlines several approaches for dealing with meta-analyses of count outcome data. These counts are the accumulation of occurred events, and these events might be rare, so a special feature of the meta-analysis is dealing with low counts including zero-count studies. Emphasis is put on approaches which are state of the art for count data modelling including mixed log-linear (Poisson) and mixed logistic (binomial) regression as well as nonparametric mixture models for count data of Poisson and binomial type. A simulation study investigates the performance and capability of discrete mixture models in estimating effect heterogeneity. The approaches are exemplified on a meta-analytic case study investigating the acceptance of bibliotherapy.

The effects of recombinant human insulin-like growth factor-1/insulin-like growth factor binding protein-3 administration on lipid and carbohydrate metabolism in recreational athletes.

OBJECTIVE: Previous studies suggested that recombinant human IGF-1 (rhIGF-1) administration affects carbohydrate and lipid metabolism in healthy people and in people with diabetes. This study aimed to determine the effects of rhIGF-1/rhIGF binding protein-3 (rhIGFBP-3) administration on glucose homeostasis and lipid metabolism in healthy recreational athletes. DESIGN AND SETTING: Randomized, double-blind, placebo-controlled rhIGF-1/rhIGFBP-3 administration study at Southampton General Hospital, UK. PARTICIPANTS: 56 recreational athletes (30 men, 26 women). METHODS: Participants were randomly assigned to receive placebo, low-dose rhIGF-1/rhIGFBP-3 (30 mg/day) or high-dose rhIGF-1/rhIGFBP-3 (60 mg/day) for 28 days. The following variables were measured before and immediately after the treatment period: fasting lipids, glucose, insulin, C-peptide and glycated haemoglobin. The homeostatic model assessment (HOMA-IR) was used to estimate insulin sensitivity and indirect calorimetry to assess substrate oxidation rates. The general linear model approach was used to compare treatment group changes with the placebo group. RESULTS: Compared with the placebo group, there was a significant reduction in fasting triglycerides in participants treated with high-dose rhIGF-1/rhIGFBP-3 (p = .030), but not in the low-dose group (p = .390). In women, but not in men, there were significant increases in total cholesterol (p = .003), HDL cholesterol (p = .001) and LDL cholesterol (p = .008). These lipid changes were associated with reduced fasting insulin (p = .010), C-peptide (p = .001) and HOMA-IR (p = .018) in women and reduced C-peptide (p = .046) in men. CONCLUSIONS: rhIGF-1/rhIGFBP-3 administration for 28 days reduced insulin concentration, improved insulin sensitivity and had significant effects on lipid profile including decreased fasting triglycerides.

Exact statistical calculation of the uncertainty term in the decision limits of the GH-2000 score for growth hormone misuse (doping) detection.

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 sensitive markers, insulin-like growth factor-I and the amino-terminal pro-peptide of type III collagen. It also includes a term to adjust for the age of the athlete. Decision limits for the GH-2000 score have been developed and are incorporated into the guidelines of the World Anti-Doping Agency. These decision limits are derived by setting a 1 in 10,000 false-positive rate rule. As these decision limits are estimated from samples of GH-2000 scores, they carry uncertainty. In previous work, this uncertainty has been addressed by establishing an upper 95% confidence interval for the true decision limits based on a normal approximation which has been shown to be appropriate if sample sizes are large (such as 1000 and above). Here, we show that these approximations, whether reasonable or not, can be entirely avoided by developing an upper 95% confidence interval for the true decision limits using an approach based upon the t-distribution. While there are considerable differences for smaller sample sizes, these become negligible when the sample size is large such as 1000 and above.

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.

Why do endocrine profiles in elite athletes differ between sports?

BACKGROUND: Endocrine profiles have been measured on blood samples obtained immediately post-competition from 693 elite athletes from 15 Olympic Sports competing at National or International level; four were subsequently excluded leaving 689 for the current analysis. METHODS: Body composition was measured by bioimpedance in a sub-set of 234 (146 men and 88 women) and from these data a regression model was constructed that enabled 'estimated' lean body mass and fat mass to be calculated on all athletes. One way ANOVA was used to assess the differences in body composition and endocrine profiles between the sports and binary logistical regression to ascertain the characteristic of a given sport compared to the others. RESULTS: The results confirmed many suppositions such as basketball players being tall, weightlifters short and cross-country skiers light. The hormone profiles were more surprising with remarkably low testosterone and free T3 (tri-iodothyronine) in male powerlifters and high oestradiol, SHBG (sex hormone binding globulin) and prolactin in male track and field athletes. Low testosterone concentrations were seen 25.4% of male elite competitors in 12 of the 15 sports and high testosterone concentrations in 4.8% of female elite athletes in 3 of the 8 sports tested. Interpretation of the results is more difficult; some of the differences between sports are at least partially due to differences in age of the athletes but the apparent differences between sports remain significant after adjusting for age. The prevalence of 'hyperandrogenism' (as defined by the IAAF (International Association of Athletics Federations) and IOC (International Olympic Committee)) amongst this cohort of 231 elite female athletes was the highest so far recorded and the very high prevalence of 'hypoandrogenism' in elite male athletes a new finding. CONCLUSIONS: It is unclear whether the differences in hormone profiles between sports is a reason why they become elite athletes in that sport or is a consequence of the arduous processes involved. For components of body composition we know that most have a major genetic component and this may well be true for endocrine profiles.

Novel markers to detect recombinant human insulin-like growth factor-I (rhIGF-I)/rhIGF binding protein-3 (rhIGFBP-3) misuse in athletes.

Insulin-like growth factor-I (IGF-I) is abused by elite athletes for its metabolic and anabolic effects. We have previously shown that it is possible to detect IGF-I misuse by measuring serum IGF-I and procollagen type III amino-terminal propeptide (P-III-NP) but a pilot study suggested measuring IGF-II, IGF binding protein-2 (IGFBP-2) and acid-labile subunit (ALS) may improve the detection of IGF-I administration. The aim of the study was to assess this in a randomized controlled trial. Twenty-six female and 30 male recreational athletes were randomized to 28 days' treatment with placebo or recombinant human (rh)IGF-I/rhIGF binding protein-3 (IGFBP-3) complex (30 mg/day or 60 mg/day), followed by 56 days' washout. IGF-II, IGFBP-2 and ALS (women only) were measured using commercial immunoassays. IGFBP-2 increased and IGF-II decreased in response to both low and high dose rhIGF-I/rhIGFBP-3 in both women and men while ALS decreased in women in response to high dose rhIGF-I/rhIGFBP-3. Two days after discontinuing treatment, significant differences remained between the three treatment groups in IGFBP-2 and IGF-II, but not ALS. Thereafter there were no significant differences between the three treatment groups in any of the markers. Combining IGF-I with IGF-II and/or IGFBP-2 improved the performance of the test to detect rhIGF-I/rhIGFBP-3 administration in both women and men. Copyright © 2016 John Wiley & Sons, Ltd.

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.

The covariate-adjusted frequency plot.

Count data arise in numerous fields of interest. Analysis of these data frequently require distributional assumptions. Although the graphical display of a fitted model is straightforward in the univariate scenario, this becomes more complex if covariate information needs to be included into the model. Stratification is one way to proceed, but has its limitations if the covariate has many levels or the number of covariates is large. The article suggests a marginal method which works even in the case that all possible covariate combinations are different (i.e. no covariate combination occurs more than once). For each covariate combination the fitted model value is computed and then summed over the entire data set. The technique is quite general and works with all count distributional models as well as with all forms of covariate modelling. The article provides illustrations of the method for various situations and also shows that the proposed estimator as well as the empirical count frequency are consistent with respect to the same parameter.

The development of decision limits for the GH-2000 detection methodology using additional insulin-like growth factor-I and amino-terminal pro-peptide of type III collagen assays.

The GH-2000 and GH-2004 projects have developed a method for detecting GH misuse based on measuring insulin-like growth factor-I (IGF-I) and the amino-terminal pro-peptide of type III collagen (P-III-NP). The objectives were to analyze more samples from elite athletes to improve the reliability of the decision limit estimates, to evaluate whether the existing decision limits needed revision, and to validate further non-radioisotopic assays for these markers. The study included 998 male and 931 female elite athletes. Blood samples were collected according to World Anti-Doping Agency (WADA) guidelines at various sporting events including the 2011 International Association of Athletics Federations (IAAF) World Athletics Championships in Daegu, South Korea. IGF-I was measured by the Immunotech A15729 IGF-I IRMA, the Immunodiagnostic Systems iSYS IGF-I assay and a recently developed mass spectrometry (LC-MS/MS) method. P-III-NP was measured by the Cisbio RIA-gnost P-III-P, Orion UniQ™ PIIINP RIA and Siemens ADVIA Centaur P-III-NP assays. The GH-2000 score decision limits were developed using existing statistical techniques. Decision limits were determined using a specificity of 99.99% and an allowance for uncertainty because of the finite sample size. The revised Immunotech IGF-I - Orion P-III-NP assay combination decision limit did not change significantly following the addition of the new samples. The new decision limits are applied to currently available non-radioisotopic assays to measure IGF-I and P-III-NP in elite athletes, which should allow wider flexibility to implement the GH-2000 marker test for GH misuse while providing some resilience against manufacturer withdrawal or change of assays.

Meta-analysis and meta-modelling for diagnostic problems.

BACKGROUND: A proportional hazards measure is suggested in the context of analyzing SROC curves that arise in the meta-analysis of diagnostic studies. The measure can be motivated as a special model: the Lehmann model for ROC curves. The Lehmann model involves study-specific sensitivities and specificities and a diagnostic accuracy parameter which connects the two. METHODS: A study-specific model is estimated for each study, and the resulting study-specific estimate of diagnostic accuracy is taken as an outcome measure for a mixed model with a random study effect and other study-level covariates as fixed effects. The variance component model becomes estimable by deriving within-study variances, depending on the outcome measure of choice. In contrast to existing approaches - usually of bivariate nature for the outcome measures - the suggested approach is univariate and, hence, allows easily the application of conventional mixed modelling. RESULTS: Some simple modifications in the SAS procedure proc mixed allow the fitting of mixed models for meta-analytic data from diagnostic studies. The methodology is illustrated with several meta-analytic diagnostic data sets, including a meta-analysis of the Mini-Mental State Examination as a diagnostic device for dementia and mild cognitive impairment. CONCLUSIONS: The proposed methodology allows us to embed the meta-analysis of diagnostic studies into the well-developed area of mixed modelling. Different outcome measures, specifically from the perspective of whether a local or a global measure of diagnostic accuracy should be applied, are discussed as well. In particular, variation in cut-off value is discussed together with recommendations on choosing the best cut-off value. We also show how this problem can be addressed with the proposed methodology.

Revisiting Youden's index as a useful measure of the misclassification error in meta-analysis of diagnostic studies.

The paper considers meta-analysis of diagnostic studies that use a continuous score for classification of study participants into healthy or diseased groups. Classification is often done on the basis of a threshold or cut-off value, which might vary between studies. Consequently, conventional meta-analysis methodology focusing solely on separate analysis of sensitivity and specificity might be confounded by a potentially unknown variation of the cut-off value. To cope with this phenomena it is suggested to use, instead, an overall estimate of the misclassification error previously suggested and used as Youden's index and; furthermore, it is argued that this index is less prone to between-study variation of cut-off values. A simple Mantel-Haenszel estimator as a summary measure of the overall misclassification error is suggested, which adjusts for a potential study effect. The measure of the misclassification error based on Youden's index is advantageous in that it easily allows an extension to a likelihood approach, which is then able to cope with unobserved heterogeneity via a nonparametric mixture model. All methods are illustrated at hand of an example on a diagnostic meta-analysis on duplex doppler ultrasound, with angiography as the standard for stroke prevention.