Grouping methods for estimating the prevalences of rare traits from complex survey data that preserve confidentiality of respondents. Stat Med 2018 Jun 15;37(13):2174-2186
Date
03/27/2018Pubmed ID
29579785Pubmed Central ID
PMC6682315DOI
10.1002/sim.7648Scopus ID
2-s2.0-85044421219 (requires institutional sign-in at Scopus site) 2 CitationsAbstract
Originally, 2-stage group testing was developed for efficiently screening individuals for a disease. In response to the HIV/AIDS epidemic, 1-stage group testing was adopted for estimating prevalences of a single or multiple traits from testing groups of size q, so individuals were not tested. This paper extends the methodology of 1-stage group testing to surveys with sample weighted complex multistage-cluster designs. Sample weighted-generalized estimating equations are used to estimate the prevalences of categorical traits while accounting for the error rates inherent in the tests. Two difficulties arise when using group testing in complex samples: (1) How does one weight the results of the test on each group as the sample weights will differ among observations in the same group. Furthermore, if the sample weights are related to positivity of the diagnostic test, then group-level weighting is needed to reduce bias in the prevalence estimation; (2) How does one form groups that will allow accurate estimation of the standard errors of prevalence estimates under multistage-cluster sampling allowing for intracluster correlation of the test results. We study 5 different grouping methods to address the weighting and cluster sampling aspects of complex designed samples. Finite sample properties of the estimators of prevalences, variances, and confidence interval coverage for these grouping methods are studied using simulations. National Health and Nutrition Examination Survey data are used to illustrate the methods.
Author List
Hyun N, Gastwirth JL, Graubard BIMESH terms used to index this publication - Major topics in bold
Cluster AnalysisConfidentiality
Epidemiologic Methods
Health Surveys
Humans
Models, Statistical
Prevalence
Sampling Studies
Statistics as Topic
