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Activity Number: 527 - Diagnostic Tests: Student Papers and Correlated Data
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Medical Devices and Diagnostics
Abstract #330862 Presentation
Title: On Statistical Inference in Factorial Multi-Reader Studies Using Bootstrap
Author(s): Andriy Bandos*
Companies: University of Pittsburgh
Keywords: ROC analysis; fully-crossed design; multi-reader studies; diagnostic imaging; cross-correlated data; bootstrap
Abstract:

Imaging technologies relying on interpretations of trained professionals are typically evaluated in fully crossed multi-reader studies. In these settings, multi-sample bootstrap can be used to account for factor-specific variability and correlation, and to perform simple asymptotic inference for non-linear summary measures such as the AUC. Previous studies tend to disfavor the multi-reader bootstrap approach because of the substantial upward bias of the estimated variance. However, relative properties of the corresponding statistical inference are not known. We developed a general approach for structuring bias of the multi-reader bootstrap variance and proved that elimination of most of the bias leads to the current standard-of-practice test. Simulation study shows that the resulting nearly-unbiased variance estimator requires using t-distribution to control the type I error in statistical testing, which in some settings compromises power. Bootstrap's upward bias plays a protective role in Wald-type inference which enables higher power when between-reader variability is high. Additional gain can be achieved, without compromising type I error, by eliminating only a part of the bias.


Authors who are presenting talks have a * after their name.

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