Hospital Mortality Rate Estimation for Public Reporting

• Activity Number: 398
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Edward I. George* and Veronika Rockova and Ville Satoppa and Paul Rosenbaum and Jeffrey Silber and Nabanita Mukherjee
• Companies: The Wharton School and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and Novartis Healthcare Pvt. Ltd.
• Keywords: hierarchical Bayes ; shrinkage estimation ; logistic regression ; random effects ; direct standardization ; matched comparisons

Abstract

For the estimation of many parameters, borrowing statistical strength across related observations via hierarchical modeling has been one of the most powerful statistical developments over the past 60 years. The phenomenon is especially evident in classical frameworks where dominating ensemble information shrinks noisy parameter estimates to an overall mean. This is exactly what happened with Medicare's Hospital Compare random effects model, which asserted that 99.5% of hospital mortality rates for acute myocardial infarction (AMI) were ''no different than the U.S. national rate''. In this talk we shall see that their conclusions stemmed from seemingly innocuous, though controversial, assumptions about ensemble means and variances that were at odds with the data. As an alternative, we propose hierarchical random effects models with flexible prior structure that emancipate the means and variances and yield dramatically different conclusions. The superior calibration of our models is demonstrated with comparisons based on predictive Bayes factors and predictive matched samples. Finally, direct rather than indirect standardization is seen to be superior for public reporting.