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Performance assessment as an application of causal inference

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  • Nicholas T. Longford

Abstract

Institutions in healthcare, education and other public services are under constant pressure to perform to high standards and with efficiency. Assessment of their performance is often problematic because it either ignores important background variables of their patients, students or clients (the casemix), or adjusts for them in a way that is not equitable or transparent. We apply a method of indirect standardization motivated by the potential outcomes framework, in which we consider the hypothetical scenario of an institution's case‐load being dispersed for treatment throughout the domain of assessment (the country's institutions). The target of estimation is the difference of the means of the outcomes in the realized and hypothetical settings. The method is applied to estimating the prevalence of bronchopulmonary dysplasia in extremely preterm‐born infants in the neonatal units and their networks in Great Britain in 2017. The prevalence of bronchopulmonary dysplasia is an audit measure in the UK National Neonatal Audit Programme.

Suggested Citation

  • Nicholas T. Longford, 2020. "Performance assessment as an application of causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1363-1385, October.
    • Handle: RePEc:bla:jorssa:v:183:y:2020:i:4:p:1363-1385
    DOI: 10.1111/rssa.12529
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    References listed on IDEAS

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    1. Paul R. Rosenbaum, 2004. "Design sensitivity in observational studies," Biometrika, Biometrika Trust, vol. 91(1), pages 153-164, March.
    2. Rosenbaum, Paul R., 2010. "Design Sensitivity and Efficiency in Observational Studies," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 692-702.
    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. George Leckie & Harvey Goldstein, 2009. "The limitations of using school league tables to inform school choice," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(4), pages 835-851, October.
    5. Jan Hannig & Thomas C. M. Lee, 2009. "Generalized fiducial inference for wavelet regression," Biometrika, Biometrika Trust, vol. 96(4), pages 847-860.
    6. Harvey Goldstein & David J. Spiegelhalter, 1996. "League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 385-409, May.
    7. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, October.
    8. E. I. George & V. Ročková & P. R. Rosenbaum & V. A. Satopää & J. H. Silber, 2017. "Mortality Rate Estimation and Standardization for Public Reporting: Medicare’s Hospital Compare," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 933-947, July.
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