Bayesian methods for diagnostic test studies

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This proposal describes the development of Bayesian statistical methods to be used for 1) the evaluation, and 2) the interpretation of medical diagnostic tests, and 3) the design of studies involving them. The projects described here build upon my previous work on Bayesian methods for imperfect tests. Under the theme on evaluation, methods will be described for i) estimation of a hierarchical latent class model for adjusting for conditional dependence, and ii) comparing competing methods for adjusting for conditional dependence, and iii) evaluation of a new diagnostic test in the presence of both verification bias and an imperfect reference test. Under the theme on interpretation, methods will be described for i) analyzing clinical trial results based on a composite result from multiple imperfect tests, and ii) constructing quality control charts based on multiple imperfect tests. Under the theme on study design, methods will be described for i) sample size calculation for studies evaluating three imperfect tests or using them to measure disease prevalence, and ii) sample size calculation for diagnostic test studies based on decision theoretic criteria. Though the methods described here are motivated by problems in diagnostic testing they can be applied to any area where there are errors in measurement.