JSM 2005 - Toronto

Abstract #302454

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 248
Type: Invited
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Health Policy Statistics
Abstract - #302454
Title: Quality Assessment Using Flexible Prior Distributions and Triple-Goal Estimates in Hierarchical Models
Author(s): Susan M. Paddock*+ and Greg Ridgeway and Rongheng Lin and Thomas A. Louis
Companies: RAND Corporation and RAND Corporation and Johns Hopkins University and Johns Hopkins University
Address: 1776 Main Street, Santa Monica, CA, 90401, USA
Keywords: Bayesian statistics ; hierarchical models ; nonparametrics ; ranking
Abstract:

Health care quality assessments often aim to achieve numerous goals, namely obtaining estimates of unit-specific means, ranks, and the distribution of unit-specific parameters. The Bayesian approach provides a powerful way to structure complicated models for achieving inferential goals. While no single estimate can be optional for achieving all three inferential goals listed above, the communication and credibility of results will be enhanced by reporting a single estimate that optimizes performance over all three goals simultaneously. Triple goal estimates (Shen and Louis 1998) optimize over these three goals and provide an appealing candidate estimator for quality of care assessments. Because these triple-goal estimates rely more heavily on the entire distribution than posterior means, they are more sensitive to prior misspecification. In this talk, we discuss our strategy to robustify triple-goal estimates to prior misspecification by incorporating nonparametric prior distributions for unit-specific parameters. We evaluate performance based on the correctness and efficiency of the robustified estimates under numerous scenarios.

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Revised March 2005