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Activity Number: 254 - Contributed Poster Presentations: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #306446
Title: Confusion for Good: Expanding the Bayesian Logistic Meta-Analysis from Odds Ratios to the Confusion Matrix
Author(s): Thomas Gibson*
Companies: UCLA
Keywords: meta-analysis; Bayesian methods

A standard logistic meta-analysis estimates the effect of a dichotomous covariate on the probability of an adverse outcome through the log(odds ratio). When it comes to physician decision-making, diagnostic statistics such has sensitivity, specificity, positive/negative predictive value, and positive/negative likelihood ratio are of interest. We present an extension of the standard Bayesian logistic meta-analysis model that provides inference about these diagnostic statistics. We illustrate the model in a series of meta-analyses using data from studies which look at adverse events after syncopal episodes.

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

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