Testing Hypotheses about Medical Test Accuracy: Considerations for Design and Inference Public Deposited


This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Taylor & Francis and can be found at:  http://www.tandfonline.com/toc/CJAS20/current#.VZr6rEZy2Dk


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Abstract or Summary
  • Developing new medical tests and identifying single biomarkers or panels of biomarkers with superior accuracy over existing classifiers promotes lifelong health of individuals and populations. Before a medical test can be routinely used in clinical practice, its accuracy within diseased and non-diseased populations must be rigorously evaluated. We introduce a method for sample size determination for studies designed to test hypotheses about medical test or biomarker sensitivity and specificity. We show how a sample size can be determined to guard against making type I and/or type II errors by calculating Bayes factors from multiple data sets simulated under null and/or alternative models. The approach can be implemented across a variety of study designs, including investigations into one test or two conditionally independent or dependent tests. We focus on a general setting that involves non-identifiable models for data when true disease status is unavailable due to the nonexistence of or undesirable side effects from a perfectly accurate (i.e., “gold standard”) test; special cases of the general method apply to identifiable models with or without gold-standard data. Calculation of Bayes factors is performed by incorporating prior information for model parameters (e.g., sensitivity, specificity, and disease prevalence) and augmenting the observed test-outcome data with unobserved latent data on disease status to facilitate Gibbs sampling from posterior distributions. We illustrate our methods using a thorough simulation study and an application to toxoplasmosis.
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  • Branscum, A. J., Cheng, D., & Lee, J. J. (2015). Testing hypotheses about medical test accuracy: considerations for design and inference. Journal of Applied Statistics, 42(5), 1106-1119. doi:10.1080/02664763.2014.995608
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Last modified: 10/27/2017

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