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Updated: Oct 19, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Competing risks in quality and safety research: a framework to guide choice of analysis and improve reporting
Perla J Marang-van de Mheen1, Hein Putter2, Esther Bastiaannet3
1Department of Biomedical Data Sciences, Medical Decision Making, J10-S, Leiden University Medical Center, Leiden, The Netherlands p.j.marang-van_de_mheen@lumc.nl.
Abstract:
When comparing hospitals on their readmission rates as currently done in the Hospital Readmission and Reduction Program (HRRP) in the USA, should we include the competing risk of mortality after discharge, which precludes the readmission, in the analysis? Not including competing risks in current HRRP metrics was raised recently as a limitation with possible unintended consequences, as financial penalties for higher readmission rates are more severe than for higher mortality rates. Incorrectly including or ignoring competing risks can both induce bias. In this paper, we present a framework to clarify situations when competing risks should be taken into account and when they should not. We argue that the research question and the perspective from which it is asked determine whether the competing risk is also of interest and should be included in the analysis, or if only the event of interest should be considered. This information is often not explicitly reported but is needed to interpret whether the results are valid. Using the examples of readmissions and cancer, we show how different research questions fit different perspectives from which these are asked (patient, system, regulatory/insurance). Slightly changing the research question or perspective may thus change the analysis. Even though some may argue that any introduced bias is likely to be small, in the context of the HRRP, even small changes may mean that a hospital will face (higher) financial penalties. The impact of getting it wrong matters.
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