Performance Assessment as an Application of Causal Inference

Nicholas T. Longford

doi:10.1111/rssa.12529

journal article Oct 29, 2019

Performance Assessment as an Application of Causal Inference

Journal of the Royal Statistical Society Series A: Statistics in Society Vol. 183 No. 4 pp. 1363-1385 · Oxford University Press (OUP)

View at Publisher Save 10.1111/rssa.12529

Abstract

SummaryInstitutions 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.

Topics

No keywords indexed for this article. Browse by subject →

References

35

[1]

American Academy of Pediatrics "Levels of neonatal care: policy statement" Pediatrics (2014)

[2]

American Academy of Pediatrics (2015)

[3]

Berger (1985) 10.1007/978-1-4757-4286-2

[4]

DeGroot (1970)

[5]

Fisher "The fiducial argument in statistical inference" Ann. Eugen. (1935) 10.1111/j.1469-1809.1935.tb02120.x

[6]

Mortality Rate Estimation and Standardization for Public Reporting: Medicare’s Hospital Compare

E. I. George, V. Ročková, P. R. Rosenbaum et al.

Journal of the American Statistical Association 2017 10.1080/01621459.2016.1276021

[7]

League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance

Harvey Goldstein, David J. Spiegelhalter

Journal of the Royal Statistical Society Series A:... 1996 10.2307/2983325

[8]

Hannig "On generalized fiducial inference" Statist. Sin. (2009)

[9]

Healthcare Quality Improvement Partnerships (2017)

[10]

Statistics and Causal Inference

Paul W. Holland

Journal of the American Statistical Association 1986 10.1080/01621459.1986.10478354

[11]

Imbens (2015) 10.1017/cbo9781139025751

[12]

Jones "The identification of “unusual” health-care providers from a hierarchical model" Am. Statistn (2011) 10.1198/tast.2011.10190

[13]

Leckie "The limitations of using school league tables to inform school choice" J. R. Statist. Soc. (2009) 10.1111/j.1467-985x.2009.00597.x

[14]

Fiducial Distributions and Bayes' Theorem

D. V. Lindley

Journal of the Royal Statistical Society Series B:... 1958 10.1111/j.2517-6161.1958.tb00278.x

[15]

Lindley (1985)

[16]

Longford "On standard errors of model-based small-area estimators" Surv. Methodol. (2007)

[17]

Longford (2013) 10.1007/978-3-642-40433-7

[18]

Longford "Policy-oriented small-area estimation" Sth Afr. J. Statist. (2015)

[19]

Longford "Estimation under model uncertainty" Statist. Sin. (2017)

[20]

Longford "Decision theory for comparing institutions" Statist. Med. (2018) 10.1002/sim.7525

[21]

Pimentel "Large sparse optimal matching with R package rcbalance" Observnl Stud. (2016) 10.1353/obs.2016.0006

[22]

Rosenbaum "Design sensitivity in observational studies" Biometrika (2004) 10.1093/biomet/91.1.153

[23]

Rosenbaum (2010) 10.1007/978-1-4419-1213-8

[24]

Rosenbaum (2017) 10.4159/9780674982697

[25]

The central role of the propensity score in observational studies for causal effects

Paul R. Rosenbaum, Donald B. Rubin

Biometrika 1983 10.1093/biomet/70.1.41

[26]

Royal College of Pediatrics and Child Health (2018)

[27]

Rubin "Which ifs have causal answers" J. Am. Statist. Ass. (1986)

[28]

For objective causal inference, design trumps analysis

Donald B. Rubin

The Annals of Applied Statistics 2008 10.1214/08-aoas187

[29]

Matching Using Estimated Propensity Scores: Relating Theory to Practice

Donald B. Rubin, Neal Thomas

Biometrics 1996 10.2307/2533160

[30]

Shen "Triple-goal estimates in two-stage hierarchical models" J. R. Statist. Soc. (1998) 10.1111/1467-9868.00135

[31]

Silber "Indirect standardization matching: assessing specific advantage and risk synergy" Hlth Serv. Res. (2016) 10.1111/1475-6773.12470

[32]

Silber "Template matching for auditing hospital cost and quality" Hosp. Serv. Res. (2014) 10.1111/1475-6773.12156

[33]

Spiegelhalter "Funnel plots for comparing institutional performance" Statist. Med. (2005) 10.1002/sim.1970

[34]

Spiegelhalter "Statistical methods for healthcare regulation: rating, screening and surveillance (with discussion)" J. R. Statist. Soc. (2012) 10.1111/j.1467-985x.2011.01010.x

[35]

Matching Methods for Causal Inference: A Review and a Look Forward

Elizabeth A. Stuart

Statistical Science 2010 10.1214/09-sts313

Metrics

8

Citations

35

References

Details

Published: Oct 29, 2019
Vol/Issue: 183(4)
Pages: 1363-1385
License: View

Authors

N

Nicholas T. Longford

Imperial College London UK

Cite This Article

Nicholas T. Longford (2019). Performance Assessment as an Application of Causal Inference. Journal of the Royal Statistical Society Series A: Statistics in Society, 183(4), 1363-1385. https://doi.org/10.1111/rssa.12529

Performance Assessment as an Application of Causal Inference

You May Also Like