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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Predictive abilities comparison from multiple dynamic prediction models.

Clémence Moreau1, Jérémie Riou2,3, Marine Roux1

  • 1UPRES 3859, SFR 4208, HIFIH, Angers University, Angers, France.

Statistical Methods in Medical Research
|July 25, 2023
PubMed
Summary

This study introduces a new method for comparing multiple dynamic predictions of patient outcomes, enhancing personalized medicine. The approach uses updated patient information for more accurate prognostic assessments in clinical settings.

Keywords:
Competing risksdynamic Brier scoredynamic area under the receiver operating characteristic curvedynamic predictionmultiple testsprediction accuracy

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Personalized Medicine

Background:

  • Personalized medicine necessitates accurate individual prognosis.
  • Dynamic models are crucial for updating patient information during monitoring.
  • Current methods for comparing predictive abilities are limited to two comparisons.

Purpose of the Study:

  • To develop a novel procedure for multiple comparisons of dynamic predictive abilities.
  • To extend existing methods (dynamic AUC, Brier score) for more comprehensive biomarker evaluation.
  • To facilitate the integration of evolving patient data in prognostic assessments.

Main Methods:

  • Proposed a new statistical procedure for comparing more than two dynamic predictive abilities.
  • Utilized dynamic versions of the area under the receiver operating characteristic curve (AUC) and Brier score.
  • Assessed the performance of the new procedure through simulations and a hepatology case study.

Main Results:

  • The new procedure enables the comparison of multiple dynamic predictive abilities simultaneously.
  • Demonstrated the utility of the method in a real-world clinical application in hepatology.
  • The developed R functions are available on GitHub for broader accessibility.

Conclusions:

  • The proposed method overcomes the limitations of comparing only two predictive models.
  • Offers a more flexible and clinically relevant approach for evaluating biomarkers in personalized medicine.
  • Facilitates more robust prognostic predictions by incorporating multiple data streams over time.