Multiple Category-Lot Quality Assurance Sampling: A New Classification System with Application to Schistosomiasis Control

Background: 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. methodology: 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. principle findings: 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. conclusion/significance: 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. © 2012 olives et al.