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Decision curve analysis: a technical note.

Zhongheng Zhang1, Valentin Rousson2, Wen-Chung Lee3

  • 1Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.

Annals of Translational Medicine
|September 14, 2018
PubMed
Summary
This summary is machine-generated.

Decision curve analysis (DCA) assesses the clinical utility of prediction models by weighing treatment benefits against harms. This technical note details performing DCA in R, including overfitting correction and model comparison methods.

Keywords:
Decision curve analysis (DCA)diagnostic testoutcomeprediction model

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Area of Science:

  • Medical Statistics
  • Health Informatics
  • Clinical Epidemiology

Background:

  • Multivariable regression models are crucial for medical diagnosis and prediction.
  • Traditional metrics like sensitivity and specificity do not fully capture clinical utility.
  • Decision curve analysis (DCA) offers a framework to evaluate model utility by considering clinical value.

Discussion:

  • This technical note provides guidance on implementing DCA in the R statistical environment.
  • It details using the ggplot2 system for visualizing decision curves.
  • Methods for correcting model overfitting, such as bootstrap and cross-validation, are explained.

Key Insights:

  • DCA quantifies clinical utility by calculating net benefit across various threshold probabilities.
  • The method incorporates patient or policymaker preferences via threshold probability.
  • Bootstrap methods are used for calculating confidence intervals and P-values for model comparisons.

Outlook:

  • The article introduces the Average Deviation About the Probability Threshold (ADAPT) as a novel index for model utility assessment.
  • This work facilitates robust evaluation and comparison of medical prediction models.
  • Future research may further refine utility metrics and their application in clinical decision-making.