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Biostatistics. 2014 Oct;15(4):651-64. doi: 10.1093/biostatistics/kxu019. Epub 2014 May 8.

On shrinkage and model extrapolation in the evaluation of clinical center performance.

Author information

  • 1Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium machteld.varewyck@ugent.be.
  • 2Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium.
  • 3Department of Statistics, Umeå University, 901 87 Umeå, Sweden.


We consider statistical methods for benchmarking clinical centers based on a dichotomous outcome indicator. Borrowing ideas from the causal inference literature, we aim to reveal how the entire study population would have fared under the current care level of each center. To this end, we evaluate direct standardization based on fixed versus random center effects outcome models that incorporate patient-specific baseline covariates to adjust for differential case-mix. We explore fixed effects (FE) regression with Firth correction and normal mixed effects (ME) regression to maintain convergence in the presence of very small centers. Moreover, we study doubly robust FE regression to avoid outcome model extrapolation. Simulation studies show that shrinkage following standard ME modeling can result in substantial power loss relative to the considered alternatives, especially for small centers. Results are consistent with findings in the analysis of 30-day mortality risk following acute stroke across 90 centers in the Swedish Stroke Register.

© The Author 2014. Published by Oxford University Press.


Causal inference; Double robustness; Firth correction; Profiling center performance; Propensity score; Quality of care; Random and fixed effects

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