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Cluster designs to assess the prevalence of acute malnutrition by lot quality assurance sampling: a validation study by computer simulation
Authors:Casey Olives  Marcello Pagano  Megan Deitchler  Bethany L Hedt  Kari Egge  Joseph J Valadez
Institution:Harvard School of Public Health, Boston, USA;
Academy for Educational Development, Washington DC, USA;
Harvard School of Public Health, Boston, USA;
American Red Cross, Bangkok, Thailand;
Liverpool School of Tropical Medicine, UK
Abstract: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.
Keywords:Acute malnutrition  Emergency  Lot quality assurance sampling  Sequential sampling  Wasting
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