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Updated: May 28, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
A Bayesian approach to risk-adjusted outcome monitoring in healthcare
1Department of Industrial and Manufacturing Systems Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA. lzeng@uta.edu
Abstract:
Clinical outcomes are commonly monitored in healthcare practices to detect changes in care providers' performance. One key challenge in outcome monitoring is the need of adjustment for patient base-line risks. Various control charting methods have been developed to conduct risk-adjusted outcome monitoring, but they all rely on the availability of a large number of historical data. We propose a Bayesian approach to this type of monitoring for cases where historical data are not available. In our approach, detection of change is formulated as a model-selection problem and solved using a popular Bayesian tool for variable selection, the Bayes factor. Issues in decision-making about whether there is a change point in the observed patient outcomes are addressed, including specification of priors and computation of Bayes factors. This approach is applied to a real data set on cardiac surgeries, and its performance under different parameter scenarios is studied through simulations.
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