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Activity Number: 628
Type: Invited
Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
Sponsor: Health Policy Statistics Section
Abstract #314262
Title: Hierarchical Bayesian Evaluation of Health System Change Using Administrative Data
Author(s): Frank Yoon and Mariel M. Finucane* and Lauren N. Vollmer and Randall Brown
Companies: Mathematica Policy Research and Mathematica Policy Research and Mathematica Policy Research and Mathematica Policy Research
Keywords: Bayesian methods ; Hierarchical models ; Decision making ; Multiple testing ; Heterogeneous effects ; Health policy evaluation
Abstract:

Policymakers are tasked with making decisions under uncertainty. In practice, the frequentist framework for policy evaluation focuses on testing the hypothesis that program impacts are equal to zero. Stakeholders often view the resulting 'thumbs up-thumbs down' inference as restrictive. By contrast, the Bayesian evaluation framework provides intuitive, informative, probabilistic inference such as "There is a 70% chance that the intervention improved the outcome of interest by at least 5%." Furthermore, the conventional approach to policy evaluation often tests many hypotheses separately (by outcomes, by time periods, by geographic regions). By contrast, a Bayesian model can piece together disparate data sources to obtain a more precise impact estimate, reducing the likelihood that important but modest-sized effects go unrecognized for lack of statistical power. In this talk - using data from the evaluation of an initiative of the Affordable Care Act as a motivating example - we will summarize the ways in which Bayesian methods can provide a flexible and powerful tool for policy evaluation, and we will discuss the challenge and tradeoffs of the Bayesian approach.


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

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