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Adaptive sample size re-estimation in MRMC studies
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Paper Abstract

Multi-reader multi-case (MRMC) studies are often used for the evaluation of medical imaging devices. Due to limited prior information, the sizing of such studies (i.e., sizing both readers and cases) is often inaccurate. It is therefore desirable to adaptively resize the study towards a target power after an interim analysis of the study data. The major statistical concern for sample size re-estimation based on the interim analysis is the inflation of type I error rate. We developed methods that, based upon the observed data at the interim analysis, simultaneously resize the study towards a target power and adaptively adjust the critical value for the final hypothesis testing to control the type I error rate. Our methodologies apply to commonly used study endpoints including the area under the ROC curve (AUC), sensitivity, and specificity. Simulation studies show our methods can boost the statistical power to a target value by resizing the study after an interim analysis while controlling the type I error rate at the nominal level. We have developed a freely available R software package for the design and analysis of adaptive MRMC studies.

Paper Details

Date Published: 4 March 2019
PDF: 8 pages
Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 109520G (4 March 2019); doi: 10.1117/12.2513646
Show Author Affiliations
Weijie Chen, U.S. Food and Drug Administration (United States)
Zhipeng Huang, U.S. Food and Drug Administration (United States)
Frank Samuelson, U.S. Food and Drug Administration (United States)
Lucas Tcheuko, U.S. Food and Drug Administration (United States)


Published in SPIE Proceedings Vol. 10952:
Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment
Robert M. Nishikawa; Frank W. Samuelson, Editor(s)

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