Multiple category-lot quality assurance sampling: a new classification system with application to schistosomiasis control.
Originally a binary classifier, Lot Quality Assurance Sampling (LQAS) has proven to be a useful tool for classification of the prevalence of Schistosoma mansoni into multiple categories (≤10%, >10 and <50%, ≥50%), and semi-curtailed sampling has been shown to effectively reduce the number of o...
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doaj-33efe62bde8f469083ab72ee2f98edd72020-11-25T02:33:38ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352012-01-0169e180610.1371/journal.pntd.0001806Multiple category-lot quality assurance sampling: a new classification system with application to schistosomiasis control.Casey OlivesJoseph J ValadezSimon J BrookerMarcello PaganoOriginally a binary classifier, Lot Quality Assurance Sampling (LQAS) has proven to be a useful tool for classification of the prevalence of Schistosoma mansoni into multiple categories (≤10%, >10 and <50%, ≥50%), and semi-curtailed sampling has been shown to effectively reduce the number of observations needed to reach a decision. To date the statistical underpinnings for Multiple Category-LQAS (MC-LQAS) have not received full treatment. We explore the analytical properties of MC-LQAS, and validate its use for the classification of S. mansoni prevalence in multiple settings in East Africa.We outline MC-LQAS design principles and formulae for operating characteristic curves. In addition, we derive the average sample number for MC-LQAS when utilizing semi-curtailed sampling and introduce curtailed sampling in this setting. We also assess the performance of MC-LQAS designs with maximum sample sizes of n=15 and n=25 via a weighted kappa-statistic using S. mansoni data collected in 388 schools from four studies in East Africa.Overall performance of MC-LQAS classification was high (kappa-statistic of 0.87). In three of the studies, the kappa-statistic for a design with n=15 was greater than 0.75. In the fourth study, where these designs performed poorly (kappa-statistic less than 0.50), the majority of observations fell in regions where potential error is known to be high. Employment of semi-curtailed and curtailed sampling further reduced the sample size by as many as 0.5 and 3.5 observations per school, respectively, without increasing classification error.This work provides the needed analytics to understand the properties of MC-LQAS for assessing the prevalance of S. mansoni and shows that in most settings a sample size of 15 children provides a reliable classification of schools.http://europepmc.org/articles/PMC3435238?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Casey Olives Joseph J Valadez Simon J Brooker Marcello Pagano |
spellingShingle |
Casey Olives Joseph J Valadez Simon J Brooker Marcello Pagano Multiple category-lot quality assurance sampling: a new classification system with application to schistosomiasis control. PLoS Neglected Tropical Diseases |
author_facet |
Casey Olives Joseph J Valadez Simon J Brooker Marcello Pagano |
author_sort |
Casey Olives |
title |
Multiple category-lot quality assurance sampling: a new classification system with application to schistosomiasis control. |
title_short |
Multiple category-lot quality assurance sampling: a new classification system with application to schistosomiasis control. |
title_full |
Multiple category-lot quality assurance sampling: a new classification system with application to schistosomiasis control. |
title_fullStr |
Multiple category-lot quality assurance sampling: a new classification system with application to schistosomiasis control. |
title_full_unstemmed |
Multiple category-lot quality assurance sampling: a new classification system with application to schistosomiasis control. |
title_sort |
multiple category-lot quality assurance sampling: a new classification system with application to schistosomiasis control. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Neglected Tropical Diseases |
issn |
1935-2727 1935-2735 |
publishDate |
2012-01-01 |
description |
Originally a binary classifier, Lot Quality Assurance Sampling (LQAS) has proven to be a useful tool for classification of the prevalence of Schistosoma mansoni into multiple categories (≤10%, >10 and <50%, ≥50%), and semi-curtailed sampling has been shown to effectively reduce the number of observations needed to reach a decision. To date the statistical underpinnings for Multiple Category-LQAS (MC-LQAS) have not received full treatment. We explore the analytical properties of MC-LQAS, and validate its use for the classification of S. mansoni prevalence in multiple settings in East Africa.We outline MC-LQAS design principles and formulae for operating characteristic curves. In addition, we derive the average sample number for MC-LQAS when utilizing semi-curtailed sampling and introduce curtailed sampling in this setting. We also assess the performance of MC-LQAS designs with maximum sample sizes of n=15 and n=25 via a weighted kappa-statistic using S. mansoni data collected in 388 schools from four studies in East Africa.Overall performance of MC-LQAS classification was high (kappa-statistic of 0.87). In three of the studies, the kappa-statistic for a design with n=15 was greater than 0.75. In the fourth study, where these designs performed poorly (kappa-statistic less than 0.50), the majority of observations fell in regions where potential error is known to be high. Employment of semi-curtailed and curtailed sampling further reduced the sample size by as many as 0.5 and 3.5 observations per school, respectively, without increasing classification error.This work provides the needed analytics to understand the properties of MC-LQAS for assessing the prevalance of S. mansoni and shows that in most settings a sample size of 15 children provides a reliable classification of schools. |
url |
http://europepmc.org/articles/PMC3435238?pdf=render |
work_keys_str_mv |
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