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

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 speci

SUBMITTER: Bermingham ML 

PROVIDER: S-EPMC4493568 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature