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Updated: May 7, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Cross-Sectoral Comparisons of Process Quality Indicators of Health Care Across Residential Regions Using Restricted
Hana Šinkovec1,2, Walter Gall3, Georg Heinze1
1Institute of Clinical Biometrics, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.
Background:
Practice guidelines recommend patient management based on scientific evidence. Quality indicators gauge adherence to such recommendations and assess health care quality. They are usually defined as adverse event rates, which may not fully capture guideline adherence over time.
Methods:
For assessing process indicators where compliance to the recommended treatment can be assessed by evaluating a patient's trace in linked routine databases, we propose using restricted mean survival time or restricted mean time lost, which are applicable even in competing risk situations. We demonstrate their application by assessing the compliance of patients with acute myocardial infarction (AMI) to high-power statins over 12 months in Austria's political districts, using pseudo-observations and employing causal inference methods to achieve regional comparability.
Results:
We analyzed the compliance of 31,678 AMI patients from Austria's 116 political districts with index AMI between 2011 and 2015. The results revealed considerable compliance variations across districts but also plausible spatial similarities.
Conclusions:
Restricted mean survival time and restricted mean time lost provide interpretable estimates of patients' expected time in compliance (lost), well-suited for risk-adjusted entity comparisons in the presence of (measurable) confounding, censoring, and competing risks.
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