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Modified Brier score for evaluating prediction accuracy for binary outcomes.

Wei Yang1, Jiakun Jiang2, Erin M Schnellinger1

  • 1Department of Biostatistics, Epidemiology and Informatics, 14640University of Pennsylvania Perelman School of Medicine, Philadelphia, USA.

Statistical Methods in Medical Research
|August 29, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a modified Brier score for better prediction accuracy assessment. The new measure is more sensitive for comparing models and quantifying performance improvements, especially in binary outcome predictions.

Keywords:
Brier scorebinary risk predictionbreast cancer risk

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Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • The Brier score is a common metric for evaluating binary outcome prediction accuracy.
  • Interpreting the Brier score is challenging due to its dependence on outcome prevalence.
  • Existing methods lack sensitivity in comparing model performance.

Purpose of the Study:

  • To decompose the Brier score into interpretable components.
  • To propose a modified Brier score that is independent of outcome prevalence.
  • To introduce a standardized measure for quantifying prediction performance improvement.

Main Methods:

  • Decomposition of the Brier score into mean squares and outcome variance.
  • Modification of the Brier score by removing the outcome variance component.
  • Estimation of outcome variance using a general sliding window approach.
  • Development of a standardized performance improvement measure.

Main Results:

  • The modified Brier score demonstrates increased sensitivity for comparing prediction models via simulation.
  • The proposed standardized measure effectively quantifies prediction performance gains.
  • Application to breast cancer risk data highlights differences in model performance with and without a key predictor.

Conclusions:

  • The modified Brier score offers a more robust and interpretable measure of prediction accuracy for binary outcomes.
  • The new measures facilitate more reliable model comparison and performance evaluation.
  • This approach enhances the assessment of prediction models in various scientific and clinical applications.