2014
DOI: 10.1016/j.tvjl.2014.08.002
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Bayesian versus frequentist methods for estimating true prevalence of disease and diagnostic test performance

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Cited by 8 publications
(7 citation statements)
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References 48 publications
(81 reference statements)
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“…Both traditional (frequentist) and Bayesian methods have been developed to estimate true prevalence. When the expected prevalence is low, assumptions required for frequentist approaches may not be met and unrealistic point estimates and wide confidence intervals result [ 27 , 29 ]. In this work, we use a Bayesian approach described by Joseph et al [ 30 ] and utilized through Epitools (Bayesian estimation of true prevalence from survey testing with one test) [ 31 ].…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Both traditional (frequentist) and Bayesian methods have been developed to estimate true prevalence. When the expected prevalence is low, assumptions required for frequentist approaches may not be met and unrealistic point estimates and wide confidence intervals result [ 27 , 29 ]. In this work, we use a Bayesian approach described by Joseph et al [ 30 ] and utilized through Epitools (Bayesian estimation of true prevalence from survey testing with one test) [ 31 ].…”
Section: Methodsmentioning
confidence: 99%
“…In this work, we use a Bayesian approach described by Joseph et al [ 30 ] and utilized through Epitools (Bayesian estimation of true prevalence from survey testing with one test) [ 31 ]. The accuracy of Bayesian estimates is dependent on the quality of prior knowledge regarding sensitivity and specificity of the test and expectation of the prevalence [ 29 ]. Prior estimates of the sensitivity and specificity, in all cases, were those obtained from the SERoSP1-SERoSP2 diagnostic combination on the validation dataset.…”
Section: Methodsmentioning
confidence: 99%
“…Therefore, prior information should be sought and incorporated in terms of prior distributions [ 46 , 47 ]. Additionally, the large sample size (22,126 animals) made a corrective strategy, which allowed the data “to speak for itself” [ 48 ]. In this sense, Model 1 required fewer parameters than Model 2, which would have required prior knowledge of sensitivities and specificities for both tests.…”
Section: Discussionmentioning
confidence: 99%
“…This status is given by a 'gold standard' test. In the absence of a 'gold standard' test, a Bayesian approach is helpful to estimate test Se, Sp and prevalence, as has been done for several diseases (19,20,27,28,29,30). This is also the case for estimation of the BVDV prevalence (29).…”
Section: Discussionmentioning
confidence: 99%