Methods for estimating and interpreting provider-specific standardized mortality ratios

Jiannong Liu, Thomas A. Louis, Wei Pan, Jennie Z. Ma, Allan J. Collins

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Standardized Mortality Ratios (SMRs) are widely used as a measurement of quality of care for profiling and otherwise comparing medical care providers. Invalid estimation or inappropriate interpretation may have serious local and national consequences. Estimating an SMR entails producing provider-specific expected deaths via a statistical model and then computing the "observed/expected" ratio. Appropriate comparison of estimated SMRs requires considering both estimated values and statistical uncertainty. With statistical uncertainty that varies over providers, hypothesis testing to identify poor performers unfairly penalizes large providers; use of direct estimates unfairly penalizes small providers. Since neither approach suffices, we report on a suite of comparisons, each addressing an important aspect of the comparison. Our approach is based on a hierarchical statistical model. Goals include estimating and ranking (percentiling) provider-specific SMRs and calculating the probability that a provider's true SMR percentile falls within a specified percentile range. We present the issues and related statistical models for comparing SMRs and apply our approaches to the 1998 United States Renal Data System (USRDS) dialysis provider data.

Original languageEnglish (US)
Pages (from-to)135-149
Number of pages15
JournalHealth Services and Outcomes Research Methodology
Issue number3
StatePublished - Sep 2003

Bibliographical note

Funding Information:
Work supported by the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, under contract No. N01-DK-9-2343. Authors acknowledge Roger Milam from the Centers for Medicare and Medicaid Services (CMS) and Shu-Cheng Chen and James P. Ebben at the United States Renal Data System for their assistance with CMS data.


  • Bayesian hierarchical models
  • End-Stage Renal Disease (ESRD)
  • Percentile estimates
  • Standardized Mortality Ratio (SMR)
  • United States Renal Data System (USRDS)


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