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Tracking type specific prevalence of human Papillomavirus in cervical pre-cancer: a novel sampling strategy

BACKGROUND: Surveillance designed to detect changes in the type-specific distribution of HPV in cervical intraepithelial neoplasia grade 3 (CIN-3) is necessary to evaluate the effectiveness of the Australian vaccination programme on cancer causing HPV types. This paper develops a protocol that elimi...

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Autores principales: Waters, Edward K, Kaldor, John, Hamilton, Andrew J, Smith, Anthony MA, Philp, David J, Donovan, Basil, Regan, David G
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3508807/
https://www.ncbi.nlm.nih.gov/pubmed/22697428
http://dx.doi.org/10.1186/1471-2288-12-77
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author Waters, Edward K
Kaldor, John
Hamilton, Andrew J
Smith, Anthony MA
Philp, David J
Donovan, Basil
Regan, David G
author_facet Waters, Edward K
Kaldor, John
Hamilton, Andrew J
Smith, Anthony MA
Philp, David J
Donovan, Basil
Regan, David G
author_sort Waters, Edward K
collection PubMed
description BACKGROUND: Surveillance designed to detect changes in the type-specific distribution of HPV in cervical intraepithelial neoplasia grade 3 (CIN-3) is necessary to evaluate the effectiveness of the Australian vaccination programme on cancer causing HPV types. This paper develops a protocol that eliminates the need to calculate required sample size; sample size is difficult to calculate in advance because HPV’s true type-specific prevalence is imperfectly known. METHOD: A truncated sequential sampling plan that collects a variable sample size was designed to detect changes in the type-specific distribution of HPV in CIN-3. Computer simulation to evaluate the accuracy of the plan at classifying the prevalence of an HPV type as low (< 5%), moderate (5-15%), or high (> 15%) and the average sample size collected was conducted and used to assess its appropriateness as a surveillance tool. RESULTS: The plan classified the proportion of CIN-3 lesions positive for an HPV type very accurately, with >90% of simulations correctly classifying a simulated data-set with known prevalence. Misclassifying an HPV type of high prevalence as being of low prevalence, arguably the most serious kind of potential error, occurred < 0.05 times per 100 simulations. A much lower sample size (21–22 versus 40–48) was required to classify samples of high rather than low or moderate prevalence. CONCLUSIONS: Truncated sequential sampling enables the proportion of CIN-3 due to an HPV type to be accurately classified using small sample sizes. Truncated sequential sampling should be used for type-specific HPV surveillance in the vaccination era.
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spelling pubmed-35088072012-12-03 Tracking type specific prevalence of human Papillomavirus in cervical pre-cancer: a novel sampling strategy Waters, Edward K Kaldor, John Hamilton, Andrew J Smith, Anthony MA Philp, David J Donovan, Basil Regan, David G BMC Med Res Methodol Research Article BACKGROUND: Surveillance designed to detect changes in the type-specific distribution of HPV in cervical intraepithelial neoplasia grade 3 (CIN-3) is necessary to evaluate the effectiveness of the Australian vaccination programme on cancer causing HPV types. This paper develops a protocol that eliminates the need to calculate required sample size; sample size is difficult to calculate in advance because HPV’s true type-specific prevalence is imperfectly known. METHOD: A truncated sequential sampling plan that collects a variable sample size was designed to detect changes in the type-specific distribution of HPV in CIN-3. Computer simulation to evaluate the accuracy of the plan at classifying the prevalence of an HPV type as low (< 5%), moderate (5-15%), or high (> 15%) and the average sample size collected was conducted and used to assess its appropriateness as a surveillance tool. RESULTS: The plan classified the proportion of CIN-3 lesions positive for an HPV type very accurately, with >90% of simulations correctly classifying a simulated data-set with known prevalence. Misclassifying an HPV type of high prevalence as being of low prevalence, arguably the most serious kind of potential error, occurred < 0.05 times per 100 simulations. A much lower sample size (21–22 versus 40–48) was required to classify samples of high rather than low or moderate prevalence. CONCLUSIONS: Truncated sequential sampling enables the proportion of CIN-3 due to an HPV type to be accurately classified using small sample sizes. Truncated sequential sampling should be used for type-specific HPV surveillance in the vaccination era. BioMed Central 2012-06-14 /pmc/articles/PMC3508807/ /pubmed/22697428 http://dx.doi.org/10.1186/1471-2288-12-77 Text en Copyright ©2012 Waters et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Waters, Edward K
Kaldor, John
Hamilton, Andrew J
Smith, Anthony MA
Philp, David J
Donovan, Basil
Regan, David G
Tracking type specific prevalence of human Papillomavirus in cervical pre-cancer: a novel sampling strategy
title Tracking type specific prevalence of human Papillomavirus in cervical pre-cancer: a novel sampling strategy
title_full Tracking type specific prevalence of human Papillomavirus in cervical pre-cancer: a novel sampling strategy
title_fullStr Tracking type specific prevalence of human Papillomavirus in cervical pre-cancer: a novel sampling strategy
title_full_unstemmed Tracking type specific prevalence of human Papillomavirus in cervical pre-cancer: a novel sampling strategy
title_short Tracking type specific prevalence of human Papillomavirus in cervical pre-cancer: a novel sampling strategy
title_sort tracking type specific prevalence of human papillomavirus in cervical pre-cancer: a novel sampling strategy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3508807/
https://www.ncbi.nlm.nih.gov/pubmed/22697428
http://dx.doi.org/10.1186/1471-2288-12-77
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