ArticleEmerging themes in epidemiology2013
The effect of clustering on lot quality assurance sampling: a probabilistic model to calculate sample sizes for quality assessments.
Article in Emerging themes in epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Use of Lot quality assurance sampling surveys to evaluate community health worker performance in rural Zambia: a case of Luangwa district.BMC health services research · 2017Trial
- Article
- Data for Program Management: An Accuracy Assessment of Data Collected in Household Registers by Community Health Workers in Southern Kayonza, Rwanda.Journal of community health · 2015Article
- Article
- Extending cluster lot quality assurance sampling designs for surveillance programs.Statistics in medicine · 2014Article
- New tools for evaluating LQAS survey designs.Emerging themes in epidemiology · 2014Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundTraditional Lot Quality Assurance Sampling (LQAS) designs assume observations are collected using simple random sampling. Alternatively, randomly sampling clusters of observations and then individuals within clusters reduces costs but decreases the precision of the classifications. In this paper, we develop a general framework for designing the cluster(C)-LQAS system and illustrate the method with the design of data quality assessments for the community health worker program in Rwanda.
resultsTo determine sample size and decision rules for C-LQAS, we use the beta-binomial distribution to account for inflated risk of errors introduced by sampling clusters at the first stage. We present general theory and code for sample size calculations.The C-LQAS sample sizes provided in this paper constrain misclassification risks below user-specified limits. Multiple C-LQAS systems meet the specified risk requirements, but numerous considerations, including per-cluster versus per-individual sampling costs, help identify optimal systems for distinct applications.
conclusionsWe show the utility of C-LQAS for data quality assessments, but the method generalizes to numerous applications. This paper provides the necessary technical detail and supplemental code to support the design of C-LQAS for specific programs.
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Registered trials
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