Record Details
Testing Hypotheses about Medical Test Accuracy: Considerations for Design and Inference
ScholarsArchive at Oregon State University
| Field | Value |
|---|---|
| Title | Testing Hypotheses about Medical Test Accuracy: Considerations for Design and Inference |
| Names |
Branscum, Adam J.
(creator) Cheng, Dunlei (creator) Lee, J. Jack (creator) |
| Date Issued | 2015-05-04 (iso8601) |
| Note | 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 |
| Abstract | 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. |
| Genre | Article |
| Topic | comparing diagnostic tests |
| Identifier | 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 |
