Extending cluster lot quality assurance sampling designs for surveillance programs.

ABSTRACT: Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance on the basis of the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two-stage cluster designs for surveillance sampling is often more cost-effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible nonparametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the popul

SUBMITTER: Hund L 

PROVIDER: S-EPMC4047169 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature