Sample size calculations for disease freedom and ...
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Citation
| Title | Sample size calculations for disease freedom and prevalence estimation surveys |
| Author(s) | A. Branscum, W. Johnson, I. Gardner |
| Journal | Statistics in Medicine |
| Date | 2006 |
| Volume | 25 |
| Issue | 15 |
| Start page | 2658 |
| End page | 2674 |
| Abstract | 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, example: trichinellosis in Canada) and for surveys designed to estimate cluster-level prevalence of an endemic disease (prevalence estimation surveys, examples: Newcastle disease virus in chickens, Ovine progressive pneumonia in USA). 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. |
| ISSN | 0277-6715 |
Using APA 6th Edition citation style.
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