Ranking USRDS Provider Specific SMRs from 1998-2001

Published In: Health Services and Outcomes Research Methodology, v. 9, no. 1, Mar. 2009, p. 22-38

Posted on RAND.org on January 01, 2009

by Rongheng Lin, Thomas A. Louis, Susan M. Paddock, Greg Ridgeway

Read More

Access further information on this document at Springer Netherlands

This article was published outside of RAND. The full text of the article can be found at the link above.

Bayesian models coupled with optimizing a loss function provide an effective framework for computing non-standard inferences such as ranks. Inferences depend on the posterior distribution and should be guided by inferential goals. However, even optimal methods might not lead to definitive results and ranks should be accompanied by valid uncertainty assessments. The authors outline the Bayesian approach and use estimated Standardized Mortality Ratios (SMRs) in 1998-2001 from the United States Renal Data System (USRDS) as a platform to identify issues and demonstrate approaches. Our analyses extend Liu et al. (2004) by computing estimates developed by Lin et al. (2006) that minimize errors in classifying providers above or below a percentile cut-point, by combining evidence over multiple years via a first-order, autoregressive model on log (SMR), and by use of a nonparametric prior. Results show that ranks/percentiles based on maximum likelihood estimates of the SMRs and those based on testing whether an SMR = 1 substantially under-perform the optimal estimates. Combining evidence over the four years using the autoregressive model reduces uncertainty, improving performance over percentiles based on only one year. Furthermore, percentiles based on posterior probabilities of exceeding a properly chosen SMR threshold are essentially identical to those produced by minimizing classification loss. Uncertainty measures effectively calibrate performance, showing that considerable uncertainty remains even when using optimal methods. Findings highlight the importance of using loss function guided percentiles and the necessity of accompanying estimates with uncertainty assessments.

This report is part of the RAND Corporation External publication series. Many RAND studies are published in peer-reviewed scholarly journals, as chapters in commercial books, or as documents published by other organizations.

Our mission to help improve policy and decisionmaking through research and analysis is enabled through our core values of quality and objectivity and our unwavering commitment to the highest level of integrity and ethical behavior. To help ensure our research and analysis are rigorous, objective, and nonpartisan, we subject our research publications to a robust and exacting quality-assurance process; avoid both the appearance and reality of financial and other conflicts of interest through staff training, project screening, and a policy of mandatory disclosure; and pursue transparency in our research engagements through our commitment to the open publication of our research findings and recommendations, disclosure of the source of funding of published research, and policies to ensure intellectual independence. For more information, visit www.rand.org/about/principles.

The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.