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Exact statistical calculation of the uncertainty term in the decision limits of the GH2000-score for growth hormone misuse detection (doping)

Exact statistical calculation of the uncertainty term in the decision limits of the GH2000-score for growth hormone misuse detection (doping)
Exact statistical calculation of the uncertainty term in the decision limits of the GH2000-score for growth hormone misuse detection (doping)
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.
0962-2802
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
Holt, Richard
d54202e1-fcf6-4a17-a320-9f32d7024393
Boehning, Walailuck
c0e89cb8-3d21-433c-8ca1-c1ee7c5bdfa6
Guha, Nishan
1b8c142c-82be-4fd3-be11-90db4270c551
Sonsken, Peter H.
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Cowan, David.
7701ddc5-3c97-4ba0-bad3-da79ef6d7d8c
Liang, Tianyi
6710a4f3-0b42-4af1-bd3d-bfd227b3f1f4
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
Holt, Richard
d54202e1-fcf6-4a17-a320-9f32d7024393
Boehning, Walailuck
c0e89cb8-3d21-433c-8ca1-c1ee7c5bdfa6
Guha, Nishan
1b8c142c-82be-4fd3-be11-90db4270c551
Sonsken, Peter H.
566f9a8a-7019-4fe4-be60-54d2a08180bf
Cowan, David.
7701ddc5-3c97-4ba0-bad3-da79ef6d7d8c
Liang, Tianyi
6710a4f3-0b42-4af1-bd3d-bfd227b3f1f4

Bohning, Dankmar, Liu, Wei, Holt, Richard, Boehning, Walailuck, Guha, Nishan, Sonsken, Peter H., Cowan, David. and Liang, Tianyi (2017) Exact statistical calculation of the uncertainty term in the decision limits of the GH2000-score for growth hormone misuse detection (doping). Statistical Methods in Medical Research. (doi:10.1177/0962280217739452).

Record type: Article

Abstract

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.

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GH_uncert_paper_SMMR_R0 - Accepted Manuscript
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Accepted/In Press date: 16 November 2017
e-pub ahead of print date: 16 November 2017

Identifiers

Local EPrints ID: 419349
URI: https://eprints.soton.ac.uk/id/eprint/419349
ISSN: 0962-2802
PURE UUID: c277a3a5-e445-41ba-abf2-acdb6d3fdbf2
ORCID for Dankmar Bohning: ORCID iD orcid.org/0000-0003-0638-7106
ORCID for Wei Liu: ORCID iD orcid.org/0000-0002-4719-0345
ORCID for Richard Holt: ORCID iD orcid.org/0000-0001-8911-6744

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Date deposited: 11 Apr 2018 16:30
Last modified: 20 Jul 2019 01:23

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Contributors

Author: Dankmar Bohning ORCID iD
Author: Wei Liu ORCID iD
Author: Richard Holt ORCID iD
Author: Walailuck Boehning
Author: Nishan Guha
Author: Peter H. Sonsken
Author: David. Cowan
Author: Tianyi Liang

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