Bayesian Dynamic Borrowing of Historical Information with Applications to the Analysis of Large-Scale Assessments

• Published in: Psychometrika Vol. 88; no. 1; pp. 1 - 30
• Main Authors: Kaplan, David, Chen, Jianshen, Yavuz, Sinan, Lyu, Weicong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2023; Springer Nature B.V
• Subjects: Application Reviews and Case Studies; Application Reviews and Case Studies (ARCS); Assessment; Bayes Theorem; Bayesian analysis; Behavioral Science and Psychology; Case Studies; Computer Simulation; Datasets; Educational Assessment; Foreign students; Humanities; Humans; Information processing; Law; Mathematical Methods In Social Sciences; Mathematical models; Mathematics; Models, Statistical; Psychology; Psychometrics; Random variables; Research Design; Statistical Theory and Methods; Statistics for Social Sciences; Student Evaluation; Testing and Evaluation; multilevel modeling; Bayesian dynamic borrowing; large-scale assessments; power priors
• ISSN: 0033-3123; 1860-0980
• DOI: 10.1007/s11336-022-09869-3

The purpose of this paper is to demonstrate and evaluate the use of Bayesian dynamic borrowing (Viele et al, in Pharm Stat 13:41-54, 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. 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 in cases where the historical data is heterogeneous compared to the current data based on bias, mean squared error, and predictive accuracy. In cases of homogeneous historical data, Bayesian dynamic borrowing performs similarly to data pooling, Bayesian synthesis, and power priors. In contrast, for heterogeneous historical data, Bayesian dynamic borrowing performed at least as well, if not better, than other methods of borrowing with respect to mean squared error, percent bias, and leave-one-out cross-validation.