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Cluster designs to assess the prevalence of acute malnutrition by lot quality assurance sampling: a validation study by computer simulation

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  • Casey Olives
  • Marcello Pagano
  • Megan Deitchler
  • Bethany L. Hedt
  • Kari Egge
  • Joseph J. Valadez

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.

Suggested Citation

  • Casey Olives & Marcello Pagano & Megan Deitchler & Bethany L. Hedt & Kari Egge & Joseph J. Valadez, 2009. "Cluster designs to assess the prevalence of acute malnutrition by lot quality assurance sampling: a validation study by computer simulation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 495-510, April.
    • Handle: RePEc:bla:jorssa:v:172:y:2009:i:2:p:495-510
    DOI: 10.1111/j.1467-985X.2008.00572.x
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    References listed on IDEAS

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    1. Robertson, Susan E. & Valadez, Joseph J., 2006. "Global review of health care surveys using lot quality assurance sampling (LQAS), 1984-2004," Social Science & Medicine, Elsevier, vol. 63(6), pages 1648-1660, September.
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