Ranking USRDS provider specific SMRs from 1998–2001

Department of Public Health, University of Massachusetts Amherst, Rm 411 Arnold House, 715 N. Pleasant Rd., Amherst, MA 01003, USA.
Health Services and Outcomes Research Methodology 03/2009; 9(1):22-38. DOI: 10.1007/s10742-008-0040-0


Provider profiling (ranking/percentiling) is prevalent in health services research. 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. We 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.

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