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Cluster designs to assess the prevalence of acute malnutrition by lot quality assurance sampling: a validation study by computer simulation
Author(s) -
Olives Casey,
Pagano Marcello,
Deitchler Megan,
Hedt Bethany L.,
Egge Kari,
Valadez Joseph J.
Publication year - 2009
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2008.00572.x
Subject(s) - lot quality assurance sampling , cluster sampling , simple random sample , statistics , sampling (signal processing) , sample size determination , quality assurance , cluster (spacecraft) , computer science , correlation , data mining , medicine , mathematics , environmental health , external quality assessment , population , pathology , geometry , filter (signal processing) , computer vision , programming language
Summary. Traditional lot quality assurance sampling (LQAS) methods require simple random sampling to guarantee valid results. However, cluster sampling has been proposed to reduce the number of random starting points. This study uses simulations to examine the classification error of two such designs, a 67×3 (67 clusters of three observations) and a 33×6 (33 clusters of six observations) sampling scheme to assess the prevalence of global acute malnutrition (GAM). Further, we explore the use of a 67×3 sequential sampling scheme for LQAS classification of GAM prevalence. Results indicate that, for independent clusters with moderate intracluster correlation for the GAM outcome, the three sampling designs maintain approximate validity for LQAS analysis. Sequential sampling can substantially reduce the average sample size that is required for data collection. The presence of intercluster correlation can impact dramatically the classification error that is associated with LQAS analysis.