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Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data.

Bermingham ML, Handel IG, Glass EJ, Woolliams JA, de Clare Bronsvoort BM, McBride SH, Skuce RA, Allen AR, McDowell SW, Bishop SC - Sci Rep (2015)

Bottom Line: 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.Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.

View Article: PubMed Central - PubMed

Affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG.

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.

No MeSH data available.


Related in: MedlinePlus

Estimated posterior distributions of parameters from the conditional independence model with outbreak specific diagnostic sensitivities from the Northern Ireland bovine tuberculosis surveillance data.The plots depict the posterior distributions of diagnostic sensitivity and specificity of the single intradermal comparative tuberculin test (broken black line) and abattoir inspection (black line), and average true prevalence from the three Markov chain Monte Carlo chains run. The grey lines represent the prior distributions used to inform the estimates. The x-axis provides the parameter estimates and the y-axis the relative probability of taking a given value.
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f1: Estimated posterior distributions of parameters from the conditional independence model with outbreak specific diagnostic sensitivities from the Northern Ireland bovine tuberculosis surveillance data.The plots depict the posterior distributions of diagnostic sensitivity and specificity of the single intradermal comparative tuberculin test (broken black line) and abattoir inspection (black line), and average true prevalence from the three Markov chain Monte Carlo chains run. The grey lines represent the prior distributions used to inform the estimates. The x-axis provides the parameter estimates and the y-axis the relative probability of taking a given value.

Mentions: The estimated full posterior probability distributions of the test parameters are given in Fig. 1. The distributions also demonstrate that posterior estimates differ from the prior distributions of the parameter estimates; i.e. the posterior estimates were driven by the data and not dominated by the assumed prior distributions.


Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data.

Bermingham ML, Handel IG, Glass EJ, Woolliams JA, de Clare Bronsvoort BM, McBride SH, Skuce RA, Allen AR, McDowell SW, Bishop SC - Sci Rep (2015)

Estimated posterior distributions of parameters from the conditional independence model with outbreak specific diagnostic sensitivities from the Northern Ireland bovine tuberculosis surveillance data.The plots depict the posterior distributions of diagnostic sensitivity and specificity of the single intradermal comparative tuberculin test (broken black line) and abattoir inspection (black line), and average true prevalence from the three Markov chain Monte Carlo chains run. The grey lines represent the prior distributions used to inform the estimates. The x-axis provides the parameter estimates and the y-axis the relative probability of taking a given value.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493568&req=5

f1: Estimated posterior distributions of parameters from the conditional independence model with outbreak specific diagnostic sensitivities from the Northern Ireland bovine tuberculosis surveillance data.The plots depict the posterior distributions of diagnostic sensitivity and specificity of the single intradermal comparative tuberculin test (broken black line) and abattoir inspection (black line), and average true prevalence from the three Markov chain Monte Carlo chains run. The grey lines represent the prior distributions used to inform the estimates. The x-axis provides the parameter estimates and the y-axis the relative probability of taking a given value.
Mentions: The estimated full posterior probability distributions of the test parameters are given in Fig. 1. The distributions also demonstrate that posterior estimates differ from the prior distributions of the parameter estimates; i.e. the posterior estimates were driven by the data and not dominated by the assumed prior distributions.

Bottom Line: 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.Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.

View Article: PubMed Central - PubMed

Affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG.

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.

No MeSH data available.


Related in: MedlinePlus