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Friday, January 12
Fri, Jan 12, 8:30 AM - 10:15 AM
Crystal Ballroom F
Hierarchical Modeling

Borrowing Strength across Outcomes to Strengthen Impact Estimates: A Hierarchical Bayesian Approach (304177)

Randy Brown, Mathematica Policy Research 
Mariel Finucane, Mathematica Policy Research 
*Lauren Vollmer, Mathematica Policy Research 

Keywords: Bayesian; multi-outcome evaluation; hierarchical model

Health care evaluations are multi-faceted; we wish to gauge a program’s impact on not only health care costs but also utilization and quality of care. In a typical approach, we evaluate the program’s impact on each outcome independently, a fragmented strategy that increases the risk of both false positive and false negative findings. Estimating impacts simultaneously in a hierarchical Bayesian model can strengthen our conclusions by capitalizing on relationships among outcomes that typical approaches assume away. These relationships reinforce trends and negate chance findings by placing a single outcome in the context of other, related outcomes, much as more standard Bayesian approaches place a single subgroup in the context of the overall sample. Through a case study from the evaluation of the Comprehensive Primary Care Initiative (CPC), a Center for Medicare and Medicaid Innovation (CMMI) demonstration, we illustrate how borrowing strength across outcomes produces more precise estimates and a more comprehensive understanding of the intervention’s overall impact by integrating the contextual information we use to assess traditional inference into the statistical model itself.