Sample size calculations for disease freedom and prevalence estimation surveys

• Published in: Statistics in medicine Vol. 25; no. 15; pp. 2658 - 2674
• Main Authors: Branscum, Adam J., Johnson, Wesley O., Gardner, Ian A.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 15.08.2006; Wiley Subscription Services, Inc
• Subjects: Animals; Antibodies, Viral - blood; Bayes Theorem; Bayesian analysis; Bayesian modelling; Canada - epidemiology; Communicable Diseases - epidemiology; Diagnostic tests; disease freedom survey; Enzyme-Linked Immunosorbent Assay - veterinary; Epidemiologic Methods; Estimating techniques; Humans; Models, Statistical; multiple cluster prevalence estimation; Newcastle Disease - epidemiology; Newcastle Disease - virology; Newcastle disease virus - isolation & purification; prediction; Prevalence; Probability; Sample Size; Swine; Swine Diseases - epidemiology; Swine Diseases - parasitology; Switzerland - epidemiology; Trichinella spiralis - isolation & purification; Trichinellosis - epidemiology; Trichinellosis - parasitology; Trichinellosis - veterinary
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.2449

We developed a Bayesian approach to sample size calculations for studies designed to estimate disease prevalence that uses a hierarchical model for estimating the proportion of infected clusters (cluster‐level prevalence) within a country or region. The clusters may, for instance, be villages within a region, cities within a state, or herds within a country. Our model allows for clusters with zero prevalence and for variability in prevalences among infected clusters. Moreover, uncertainty about diagnostic test accuracy and within‐cluster prevalences is accounted for in the model. A predictive approach is used to address the issue of sample size selection in human and animal health surveys. We present sample size calculations for surveys designed to substantiate freedom of a region from an infectious agent (disease freedom surveys) and for surveys designed to estimate cluster‐level prevalence of an endemic disease (prevalence estimation surveys). In disease freedom surveys, for instance, assuming the cluster‐level prevalence for a particular infectious agent in the region is greater than a maximum acceptable threshold, a sample size combination consisting of the number of clusters sampled and number of subjects sampled per cluster can be determined for which authorities conducting the survey detect this excessive cluster‐level prevalence with high predictive probability. The method is straightforward to implement using the Splus/R library emBedBUGS together with WinBUGS. Copyright © 2005 John Wiley & Sons, Ltd.