Comparison of normal distribution based and nonparametric decision limits on the GH-2000 score for detecting growth hormone misuse (doping) in sport
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 (Formula presented.) or nominal FPR (Formula presented.) 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.
GH-2000 score, asymptotic distribution, decision limits, growth hormone misuse detection, nonparametric methods, tolerance intervals, tolerance limits
187-200
Liu, Wei
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Bretz, Frank
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Bohning, Dankmar
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Holt, Richard
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Böhning, W.
d862f4db-88a4-4628-b678-96d377264f72
Guha, Nishan
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Sonksen, Peter
a3249c5c-0903-472d-8054-6cffde597d44
Cowan, David
22bdafa8-cee8-481a-97d5-2687fcf325ca
January 2021
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
Bretz, Frank
51270819-e491-4a72-a410-679d86231e64
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Holt, Richard
d54202e1-fcf6-4a17-a320-9f32d7024393
Böhning, W.
d862f4db-88a4-4628-b678-96d377264f72
Guha, Nishan
786ae9e7-cd54-4a5b-b2b8-8448b701c295
Sonksen, Peter
a3249c5c-0903-472d-8054-6cffde597d44
Cowan, David
22bdafa8-cee8-481a-97d5-2687fcf325ca
Liu, Wei, Bretz, Frank, Bohning, Dankmar, Holt, Richard, Böhning, W., Guha, Nishan, Sonksen, Peter and Cowan, David
(2021)
Comparison of normal distribution based and nonparametric decision limits on the GH-2000 score for detecting growth hormone misuse (doping) in sport.
Biometrical Journal, 63 (1), .
(doi:10.1002/bimj.202000019).
Abstract
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 (Formula presented.) or nominal FPR (Formula presented.) 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.
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More information
Accepted/In Press date: 2 October 2020
e-pub ahead of print date: 9 November 2020
Published date: January 2021
Keywords:
GH-2000 score, asymptotic distribution, decision limits, growth hormone misuse detection, nonparametric methods, tolerance intervals, tolerance limits
Identifiers
Local EPrints ID: 445647
URI: http://eprints.soton.ac.uk/id/eprint/445647
ISSN: 0323-3847
PURE UUID: c3d6dd14-808c-425f-84a9-8684fe3717f1
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Date deposited: 05 Jan 2021 17:31
Last modified: 17 Mar 2024 06:01
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Contributors
Author:
Frank Bretz
Author:
W. Böhning
Author:
Nishan Guha
Author:
Peter Sonksen
Author:
David Cowan
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