Calibration: the Achilles heel of predictive analytics.

ABSTRACT:

Background

The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention.

Main text

Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice.

Conclusion

Efforts are required to avoid poor calibration when develo

SUBMITTER: Van Calster B 

PROVIDER: S-EPMC6912996 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature