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Activity Number: 202 - Meta-Analysis, Mediation, and Causal Inference from a Bayesian Perspective
Type: Contributed
Date/Time: Monday, August 8, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #320716
Title: Bayesian Dynamic Borrowing of Historical Information with Applications to the Analysis of Large-Scale Assessments
Author(s): David Kaplan*
Companies: University of Wisconsin - Madison
Keywords: Bayesian hierarchical models; Historical borrowing; Large-scale surveys

The purpose of this paper is to demonstrate and evaluate the use of Bayesian dynamic borrowing (Viele et al., 2014) as a means of systematically utilizing historical information with specific applications to large-scale educational assessments. Dynamic borrowing via Bayesian hierarchical models is a special case of a general framework of historical borrowing where the degree of borrowing depends on the heterogeneity among historical data and current data. A joint prior distribution over the historical and current data sets is specified with the degree of heterogeneity across the data sets controlled by the variance of the joint distribution. We apply Bayesian dynamic borrowing to both single-level and multilevel models and compare this approach to other historical borrowing methods such as complete pooling, Bayesian synthesis and power priors (Ibrahim & Chen, 2000). Two case studies using data from the Program for International Student Assessment reveal the utility of Bayesian dynamic borrowing in terms of predictive accuracy. This is followed by two simulation studies that reveal the utility of Bayesian dynamic borrowing over simple pooling and power priors.

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

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