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Stability of clinical prediction models developed using statistical or machine learning methods.

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  • 1Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.

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|July 19, 2023
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Summary

Clinical prediction models developed on small datasets can be unstable, leading to inaccurate risk predictions. Researchers should assess model instability using proposed methods to ensure reliable health outcome estimates.

Keywords:
calibrationfairnessprediction modelstabilityuncertainty

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Area of Science:

  • Biostatistics
  • Health Informatics
  • Clinical Epidemiology

Background:

  • Clinical prediction models are crucial for estimating individual health risks.
  • Model development is influenced by dataset size, predictors, and analytical methods.
  • Small datasets can lead to unstable models and unreliable risk predictions.

Purpose of the Study:

  • To define and assess model stability in clinical prediction models.
  • To demonstrate how model instability impacts prediction accuracy and calibration.
  • To propose methods for evaluating model instability during development.

Main Methods:

  • Defined four levels of model stability in estimated risks.
  • Utilized simulation and case studies involving statistical and machine learning models.
  • Employed bootstrap resampling to generate multiple models for instability assessment.
  • Proposed instability plots and measures, including mean absolute prediction error.

Main Results:

  • Model instability in estimated risks is often considerable, especially with small datasets.
  • Instability manifests as miscalibration of predictions when applied to new data.
  • Proposed instability assessments can reveal potential unreliability in model predictions.

Conclusions:

  • Researchers must evaluate model instability during the development phase.
  • Instability plots and measures aid in critical appraisal, fairness assessment, and validation planning.
  • Ensuring model stability is essential for trustworthy clinical risk predictions.