September 2022 Measuring performance for end-of-life care
Sebastien Haneuse, Deborah Schrag, Francesca Dominici, Sharon-Lise Normand, Kyu Ha Lee
Author Affiliations +
Ann. Appl. Stat. 16(3): 1586-1607 (September 2022). DOI: 10.1214/21-AOAS1558


Although not without controversy, readmission is entrenched as a hospital quality metric with statistical analyses generally based on fitting a logistic-Normal generalized linear mixed model. Such analyses, however, ignore death as a competing risk, although doing so for clinical conditions with high mortality can have profound effects; a hospital’s seemingly good performance for readmission may be an artifact of it having poor performance for mortality. In this paper we propose novel multivariate hospital-level performance measures for readmission and mortality that derive from framing the analysis as one of cluster-correlated semi-competing risks data. We also consider a number of profiling-related goals, including the identification of extreme performers and a bivariate classification of whether the hospital has higher-/lower-than-expected readmission and mortality rates via a Bayesian decision-theoretic approach that characterizes hospitals on the basis of minimizing the posterior expected loss for an appropriate loss function. In some settings, particularly if the number of hospitals is large, the computational burden may be prohibitive. To resolve this, we propose a series of analysis strategies that will be useful in practice. Throughout, the methods are illustrated with data from CMS on N=17,685 patients diagnosed with pancreatic cancer between 2000–2012 at one of J=264 hospitals in California.

Funding Statement

This work was supported by NIH grant R01 CA181360-01.


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Sebastien Haneuse. Deborah Schrag. Francesca Dominici. Sharon-Lise Normand. Kyu Ha Lee. "Measuring performance for end-of-life care." Ann. Appl. Stat. 16 (3) 1586 - 1607, September 2022.


Received: 1 July 2021; Revised: 1 September 2021; Published: September 2022
First available in Project Euclid: 19 July 2022

MathSciNet: MR4455892
zbMATH: 1498.62217
Digital Object Identifier: 10.1214/21-AOAS1558

Keywords: Bayesian decision theory , hierarchical modeling , provider profiling , quality of care , semicompeting risks

Rights: Copyright © 2022 Institute of Mathematical Statistics


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Vol.16 • No. 3 • September 2022
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