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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 #304330
Title: Bayesian Approach to Partially Validated Binary Regression with Response and Exposure Misclassification of Longitudinal Data
Author(s): Katrina Anderson* and James D Stamey
Companies: Marymount University and Baylor University
Keywords: Differential Misclassification; Bayesian; Validated Data; Logistic Regression

Misclassification of observational data is a common problem with adverse implications on the validity of results if not properly handled. Much research has been conducted when the response variable is misclassified, however less work has been done on the scenario in which the response variable is correlated over time and differentially misclassified. We extend previous frequentist work by investigating a Bayesian approach to four models with varying assumptions: a model that assumes differential misclassification is correlated within subjects and with the response model; a model that assumes independent response models but with differential misclassification correlated within the subject; a model with independent response processes and differential misclassification that is uncorrelated within the subject; and, lastly, a model that assumes independent response processes and that the nondifferential misclassification is uncorrelated within the subject. We compare the two approaches via estimation bias, precision, and the ability of each approach to select the “correct” model (assuming differential misclassification process is correlated with the response model and within subject).

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

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