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Journal of Medical Imaging

Multireader multicase reader studies with binary agreement data: simulation, analysis, validation, and sizing
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Paper Abstract

We treat multireader multicase (MRMC) reader studies for which a reader’s diagnostic assessment is converted to binary agreement (1: agree with the truth state, 0: disagree with the truth state). We present a mathematical model for simulating binary MRMC data with a desired correlation structure across readers, cases, and two modalities, assuming the expected probability of agreement is equal for the two modalities (P1=P2). This model can be used to validate the coverage probabilities of 95% confidence intervals (of P1, P2, or P1P2 when P1P2=0), validate the type I error of a superiority hypothesis test, and size a noninferiority hypothesis test (which assumes P1=P2). To illustrate the utility of our simulation model, we adapt the Obuchowski–Rockette–Hillis (ORH) method for the analysis of MRMC binary agreement data. Moreover, we use our simulation model to validate the ORH method for binary data and to illustrate sizing in a noninferiority setting. Our software package is publicly available on the Google code project hosting site for use in simulation, analysis, validation, and sizing of MRMC reader studies with binary agreement data.

Paper Details

Date Published: 4 December 2014
PDF: 9 pages
J. Med. Img. 1(3) 031011 doi: 10.1117/1.JMI.1.3.031011
Published in: Journal of Medical Imaging Volume 1, Issue 3
Show Author Affiliations
Weijie Chen, U.S. Food and Drug Administration (United States)
Adam Wunderlich, U.S. Food and Drug Administration (United States)
Nicholas A. Petrick, U.S. Food and Drug Administration (United States)
Brandon D. Gallas, U.S. Food and Drug Administration (United States)


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