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Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology:

Biruk Tsegaye1, Kym I E Snell2, Lucinda Archer2

  • 1Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK.

Journal of Clinical Epidemiology
|January 15, 2025
PubMed
Summary

Clinical prediction models in oncology using machine learning (ML) often lack sufficient sample size. Most studies did not justify sample size, falling short of the minimum required for regression models, indicating a likely larger deficit for ML models.

Keywords:
Machine learningMethodologyOncologyPrediction modelSample sizeSystematic review

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Area of Science:

  • Oncology
  • Biostatistics
  • Machine Learning

Background:

  • Sufficient sample size is critical for developing robust clinical prediction models.
  • Machine learning (ML) models are increasingly used for binary outcome prediction in oncology.
  • The minimum required sample size (Nmin) for regression-based models serves as a benchmark.

Purpose of the Study:

  • To review sample size justification in oncology prediction model studies using ML.
  • To compare the sample sizes used in ML model development with the minimum required sample size (Nmin) for regression models.

Main Methods:

  • A systematic search of the Medline database was conducted for ML-based prediction models in oncology published in December 2022.
  • Sample size justification was reviewed for included studies.
  • The minimum required sample size (Nmin) was calculated and compared to the sample sizes used in the studies.

Main Results:

  • Only 1 of 36 studies justified their sample size.
  • Nmin could be calculated for 17 (47%) studies; only 5 met this minimum.
  • A median deficit of 302 participants with the event was observed; ML models likely require even larger sample sizes than regression models.

Conclusions:

  • Studies developing ML prediction models in oncology rarely justify sample size.
  • Sample sizes frequently fall below Nmin, potentially leading to overfitting and imprecise risk estimates.
  • Researchers should report and meet minimum sample size requirements, especially considering the higher demands of ML models.