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Activity Number: 359 - Contributed Poster Presentations: Section on Medical Devices and Diagnostics
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Medical Devices and Diagnostics
Abstract #304119
Title: Modeling Concordance of Beta-Amyloid Images Under a Mixed Model Framework
Author(s): Katelyn A. McKenzie* and Jonathan D. Mahnken
Companies: University of Kansas Medical Center and University of Kansas Medical Center
Keywords: random effects; agreement; trinomial probabilities; Alzheimer's Disease; radiology

Imaging technologies for detecting beta-amyloid in patients with suspected Alzheimer’s disease have advanced. It is important to identify factors that contribute to diagnoses based on these images, especially qualities that contribute to reader concordance, as patients might seek a second opinion. There are statistical methods that quantify agreement, such as Cohen’s kappa statistic, but none have clear interpretations, differentiate positive and negative agreement, or account for within rater variation. We developed a method that addresses these challenges. First, a binomial mixed model with random effects for reader and image was fit. Second, random and fixed effect values were used in the trinomial distribution to obtain three probabilities of concordance: positive agreement, negative agreement and disagreement. Simulation results described concordance between readers at a range of fixed effects and demonstrated situations where disagreement may be high. Using this method, important factors, such as clinical presentation or reader training, could be identified and utilized to minimize disagreement of Alzheimer’s diagnoses based on imaging.

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

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