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Detecting and visualizing outliers in provider profiling via funnel plots and mixed effect models

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  • Francesca Ieva
  • Anna Paganoni

Abstract

In this work we propose the use of a graphical diagnostic tool (the funnel plot) to detect outliers among hospitals that treat patients affected by Acute Myocardial Infarction (AMI). We consider an application to data on AMI hospitalizations recorded in the administrative databases of our regional district. The outcome of interest is the in-hospital mortality, a variable indicating if the patient has been discharged dead or alive. We then compare the results obtained by graphical diagnostic tools with those arising from fitting parametric mixed effects models to the same data. Copyright Springer Science+Business Media New York 2015

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  • 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.
    • Handle: RePEc:kap:hcarem:v:18:y:2015:i:2:p:166-172
    DOI: 10.1007/s10729-013-9264-9
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    References listed on IDEAS

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    1. Racz, Michael J. & Sedransk, J., 2010. "Bayesian and Frequentist Methods for Provider Profiling Using Risk-Adjusted Assessments of Medical Outcomes," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 48-58.
    2. Jones, Hayley E. & Spiegelhalter, David J., 2011. "The Identification of “Unusual†Health-Care Providers From a Hierarchical Model," The American Statistician, American Statistical Association, vol. 65(3), pages 154-163.
    3. David Spiegelhalter & Christopher Sherlaw‐Johnson & Martin Bardsley & Ian Blunt & Christopher Wood & Olivia Grigg, 2012. "Statistical methods for healthcare regulation: rating, screening and surveillance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(1), pages 1-47, January.
    4. 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.
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    Cited by:

    1. Tommaso Agasisti & Francesca Ieva & Anna Maria Paganoni, 2017. "Heterogeneity, school-effects and the North/South achievement gap in Italian secondary education: evidence from a three-level mixed model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 157-180, March.
    2. Abramo, Giovanni & D’Angelo, Andrea Ciriaco & Grilli, Leonardo, 2016. "From rankings to funnel plots: The question of accounting for uncertainty when assessing university research performance," Journal of Informetrics, Elsevier, vol. 10(3), pages 854-862.
    3. Oliver Hirsch & Norbert Donner-Banzhoff & Maike Schulz & Michael Erhart, 2018. "Detecting and Visualizing Outliers in Provider Profiling Using Funnel Plots and Mixed Effects Models—An Example from Prescription Claims Data," IJERPH, MDPI, vol. 15(9), pages 1-11, September.

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