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Model-Based ROC Curve: Examining the Effect of Case Mix and Model Calibration on the ROC Plot.

Mohsen Sadatsafavi1,2, Paramita Saha-Chaudhuri3, John Petkau4

  • 1Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|October 18, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the model-based ROC (mROC) curve for assessing risk prediction model calibration during external validation. The novel mROC method and statistical test effectively separate case mix effects from miscalibration without subjective factors.

Keywords:
clinical prediction modelsmodel calibrationmodel validationreceiver-operating characteristic

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Area of Science:

  • Biostatistics
  • Predictive Analytics
  • Epidemiology

Background:

  • Risk prediction models are evaluated by discrimination and calibration.
  • Receiver-operating characteristic (ROC) curves are standard for discrimination but complicate comparisons across samples due to case mix.
  • Model calibration assessment lacks attention and often requires subjective smoothing or grouping factors.

Purpose of the Study:

  • Introduce the model-based ROC (mROC) curve for assessing model calibration and case mix effects in external validation.
  • Develop a novel statistical test for calibration that avoids subjective parameters.
  • Enhance the interpretation of ROC curves to include calibration assessment.

Main Methods:

  • Define the mROC curve as the expected ROC curve under perfect calibration in an external population.
  • Establish calibration-in-the-large and mROC-ROC curve equivalence as sufficient conditions for calibration.
  • Propose a statistical test for calibration based on mROC properties.

Main Results:

  • Demonstrate through a stylized example how mROC disentangles case mix and miscalibration.
  • Simulation studies confirm the validity and properties of the new calibration test.
  • A case study on COPD exacerbations illustrates practical application, with R code provided.

Conclusions:

  • The mROC curve offers a straightforward method to interpret case mix and calibration effects on ROC plots.
  • This framework promotes the crucial but often overlooked assessment of model calibration.
  • Applied researchers can now leverage the familiar ROC plot for comprehensive model evaluation.