Journal
STATISTICS IN MEDICINE
Volume 28, Issue 3, Pages 441-461Publisher
WILEY
DOI: 10.1002/sim.3470
Keywords
Bayesian inference; sensitivity; specificity; conditional dependence
Categories
Funding
- Natural Sciences and Engineering Research Council of Canada
- CDC Research Participation Program
- Fonds de la Recherche en Sante du Quebec
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Applications of latent class analysis in diagnostic test studies have assumed that all tests are measuring a common binary latent variable, the true disease status. In this article we describe a new approach that recognizes that tests based on different biological phenomena measure different latent variables, which in turn measure the latent true disease status. This allows for adjustment of conditional dependence between tests within disease categories. The model further allows for the inclusion of measured covariates and unmeasured random effects affecting test performance within latent classes. We describe a Bayesian approach for model estimation and describe a new posterior predictive check for evaluating candidate models. The methods are motivated and illustrated by results from a study of diagnostic tests for Chlamydia trachomatis. Published in 2008 by John Wiley & Sons, Ltd.
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