Paper
17 March 2015 Demonstration of multi- and single-reader sample size program for diagnostic studies software
Author Affiliations +
Abstract
The recently released software Multi- and Single-Reader Sample Size Sample Size Program for Diagnostic Studies, written by Kevin Schartz and Stephen Hillis, performs sample size computations for diagnostic reader-performance studies. The program computes the sample size needed to detect a specified difference in a reader performance measure between two modalities, when using the analysis methods initially proposed by Dorfman, Berbaum, and Metz (DBM) and Obuchowski and Rockette (OR), and later unified and improved by Hillis and colleagues. A commonly used reader performance measure is the area under the receiver-operating-characteristic curve. The program can be used with typical common reader-performance measures which can be estimated parametrically or nonparametrically. The program has an easy-to-use step-by-step intuitive interface that walks the user through the entry of the needed information. Features of the software include the following: (1) choice of several study designs; (2) choice of inputs obtained from either OR or DBM analyses; (3) choice of three different inference situations: both readers and cases random, readers fixed and cases random, and readers random and cases fixed; (4) choice of two types of hypotheses: equivalence or noninferiority; (6) choice of two output formats: power for specified case and reader sample sizes, or a listing of case-reader combinations that provide a specified power; (7) choice of single or multi-reader analyses; and (8) functionality in Windows, Mac OS, and Linux.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stephen L. Hillis and Kevin M. Schartz "Demonstration of multi- and single-reader sample size program for diagnostic studies software", Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 94160E (17 March 2015); https://doi.org/10.1117/12.2083150
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Error analysis

Statistical analysis

Diagnostics

Analytical research

Magnetic resonance imaging

Radiology

Data analysis

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