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Related Experiment Video

Updated: Aug 17, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Decision Curve Analysis for Personalized Treatment Choice between Multiple Options.

Konstantina Chalkou1,2, Andrew J Vickers3, Fabio Pellegrini4

  • 1Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|December 13, 2022
PubMed
Summary

This study extends decision curve analysis to network meta-analysis (NMA) for comparing personalized treatment strategies against standard approaches. The extended method helps evaluate personalized models when multiple treatments and trial evidence are available.

Keywords:
clinical usefulnessdecision curve analysisnet benefitnetwork meta-analysisprediction model

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Health Decision Science

Background:

  • Decision curve analysis (DCA) traditionally assesses personalized treatment benefit models using single trial data.
  • Evaluating personalized models becomes complex with multiple treatment options and synthesized evidence from multiple trials.

Purpose of the Study:

  • To extend decision curve analysis methodology to incorporate network meta-analysis (NMA) for scenarios with multiple treatments.
  • To compare the clinical utility of personalized treatment decision-making strategies against one-size-fit-all strategies using NMA.

Main Methods:

  • Described steps to estimate net benefit for prediction models derived from NMA.
  • Outlined a method to compare personalized strategies versus 'treat none' or 'treat all' strategies.
  • Applied the methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, comparing four treatments.

Main Results:

  • The extended DCA methodology was illustrated using various threshold values.
  • Personalized treatment strategies performed comparably or better than one-size-fit-all strategies across examined thresholds.
  • The advantage of personalized models was not consistent across all thresholds, suggesting a need for model improvement.

Conclusions:

  • This novel extension of decision curve analysis is applicable to NMA-based prediction models.
  • The methodology aids in evaluating the clinical usefulness of personalized treatment decision-making strategies.
  • Further model refinement is necessary to fully advocate for the clinical utility of personalized approaches.