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Activity Number: 88
Type: Invited
Date/Time: Sunday, July 31, 2016 : 6:00 PM to 8:00 PM
Sponsor: Biometrics Section
Abstract #318606
Title: Defining and Estimating Reliability in Hierarchical Logistic Regression Models for Health Care Provider Profiling
Author(s): Jessica Hwang and John Adams and Susan M. Paddock*
Companies: RAND Corporation and Kaiser Permanente and RAND Corporation
Keywords: hierarchical model ; reliability ; Medicare ; health care ; performance evaluation

In health care provider profiling, the reliability of a performance measure indicates whether observed differences in patient outcomes can be attributed to genuine differences in quality across providers. While reliability is easy to define, estimate, and interpret when the outcome of interest is continuous and a hierarchical linear model can be assumed, several different definitions and estimators of reliability are in use for performance measures based on binary outcomes. We compare these candidate definitions and estimators when a hierarchical logistic regression model is assumed for the binary outcome. The salient differences between various definitions are demonstrated in simulations and on a data set of Florida primary care physicians treating Medicare fee-for-service beneficiaries.

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

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