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The Use of Fixed-and Random-Effects Models for Classifying Hospitals as Mortality Outliers: A Monte Carlo Assessment

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  • Peter C. Austin
  • David A. Alter
  • Jack V. Tu

Abstract

Background. There is an increasing movement towards the release of hospital “report-cards.†However, there is a paucity of research into the abilities of the different methods to correctly classify hospitals as performance outliers.Objective.To examine the ability of risk-adjusted mortality rates computed using conventional logistic regression and random-effects logistic regression models to correctly identify hospitals that have higher than acceptable mortality.Research Design.Monte Carlo simulations.Measures.Sensitivity, specificity, and positive predictive value of a classification as a high-outlier for identifying hospitals with higher than acceptable mortality rates.Results.When the distribution of hospital-specific log-odds of death was normal, random-effects models had greater specificity and positive predictive value than fixed-effects models. However, fixed-effects models had greater sensitivity than random-effects models.Conclusions.Researchers and policy makers need to carefully consider the balance between false positives and false negatives when choosing statistical models for determining which hospitals have higher than acceptablemortality in performance profiling.

Suggested Citation

  • Peter C. Austin & David A. Alter & Jack V. Tu, 2003. "The Use of Fixed-and Random-Effects Models for Classifying Hospitals as Mortality Outliers: A Monte Carlo Assessment," Medical Decision Making, , vol. 23(6), pages 526-539, November.
  • Handle: RePEc:sae:medema:v:23:y:2003:i:6:p:526-539
    DOI: 10.1177/0272989X03258443
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    Cited by:

    1. Gutacker, Nils & Bloor, Karen & Bojke, Chris & Walshe, Kieran, 2018. "Should interventions to reduce variation in care quality target doctors or hospitals?," Health Policy, Elsevier, vol. 122(6), pages 660-666.
    2. Nils Gutacker & Andrew Street, 2018. "Multidimensional performance assessment of public sector organisations using dominance criteria," Health Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 13-27, February.
    3. Francesca Ieva & Anna Paganoni, 2015. "Detecting and visualizing outliers in provider profiling via funnel plots and mixed effect models," Health Care Management Science, Springer, vol. 18(2), pages 166-172, June.
    4. Peter C. Austin & Jack V. Tu, 2006. "Comparing clinical data with administrative data for producing acute myocardial infarction report cards," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 115-126, January.

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