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Frequentist and Bayesian approaches to prevalence estimation using examples from Johne's disease

Published online by Cambridge University Press:  17 March 2008

Locksley L. McV. Messam ,
Adam J. Branscum ,
Michael T. Collins  and
Ian A. Gardner
Locksley L. McV. Messam
Affiliation:
Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, One Shields Avenue, Davis, CA, USA
Adam J. Branscum
Affiliation:
Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY, USA
Michael T. Collins
Affiliation:
Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, USA
Ian A. Gardner*
Affiliation:
Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, One Shields Avenue, Davis, CA, USA
*
*Corresponding author. E-mail: iagardner@ucdavis.edu
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Abstract

Although frequentist approaches to prevalence estimation are simple to apply, there are circumstances where it is difficult to satisfy assumptions of asymptotic normality and nonsensical point estimates (greater than 1 or less than 0) may result. This is particularly true when sample sizes are small, test prevalences are low and imperfect sensitivity and specificity of diagnostic tests need to be incorporated into calculations of true prevalence. Bayesian approaches offer several advantages including direct computation of range-respecting interval estimates (e.g. intervals between 0 and 1 for prevalence) without the requirement of transformations or large-sample approximations. They also allow direct probabilistic interpretation, and the flexibility to model in a straightforward manner the probability of zero prevalence. In this review, we present frequentist and Bayesian methods for animal- and herd-level true prevalence estimation based on individual and pooled samples. We provide statistical methods for detecting differences between population prevalence and frequentist methods for sample size and power calculations. All examples are motivated using Mycobacterium avium subspecies paratuberculosis infection and we provide WinBUGS code for all examples of Bayesian estimation.

Keywords

prevalence estimationBayesian methodsfrequentist methodspooled testingMycobacterium avium subspecies paratuberculosis
Type
Review Article
Information
Animal Health Research Reviews , Volume 9 , Issue 1 , June 2008 , pp. 1 - 23
Copyright
Copyright © 2008 Cambridge University Press

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