Abstract
Diagnostic test sensitivity and specificity are probabilistic estimates with far reaching implications for disease control, management and genetic studies. In the absence of ‘gold standard’ tests, traditional Bayesian latent class models may be used to assess diagnostic test accuracies through the comparison of two or more tests performed on the same groups of individuals. The aim of this study was to extend such models to estimate diagnostic test parameters and true cohort-specific prevalence, using disease surveillance data. The traditional Hui-Walter latent class methodology was extended to allow for features seen in such data, including (i) unrecorded data (i.e. data for a second test available only on a subset of the sampled population) and (ii) cohort-specific sensitivities and specificities. The model was applied with and without the modelling of conditional dependence between tests. The utility of the extended model was demonstrated through application to bovine tuberculosis surveillance data from Northern and the Republic of Ireland. Simulation coupled with re-sampling techniques, demonstrated that the extended model has good predictive power to estimate the diagnostic parameters and true herd-level prevalence from surveillance data. Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.
Highlights
The observed status is linked to the unobserved true infection status
Models with herd-specific specificity were not considered further, as the realised specificity was close to unity, making the between-herd variability insignificant
Results from different formulations of the Hui-Water model for the single intradermal comparative tuberculin test (SICTT) and abattoir inspection from the Northern Ireland (NI) dataset are given in table 3 and 4
Summary
The observed status is linked to the unobserved true infection status. Estimates of sensitivities, specificities and true prevalence can be obtained using Maximum Likelihood or Bayesian techniques[2,8]. We will extend the traditional Hui-Walter latent class model[4,12] to allow for the estimation of cohort specific diagnostic test properties from the data. In our paper we have extended the traditional Hui-Walter model[4,12] which estimates diagnostic sensitivity and specificity in the absence of a gold standard test, to include two additional multinomial counts; the probabilities that individuals deemed positive and negative by the initial diagnostic test are not classified by subsequent diagnoses. We aim (i) to estimate diagnostic test parameters and true prevalence from surveillance data with some unrecorded class variables (i.e. results from a second test available only on a subset of the sampled population), and (ii) to allow for variation among sub-populations in the diagnostic test properties
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