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Regression shrinkage methods for clinical prediction models do not guarantee improved performance: Simulation study.

Ben Van Calster1,2, Maarten van Smeden2,3, Bavo De Cock1,4

  • 1Department of Development and Regeneration, KU Leuven, Leuven, Belgium.

Statistical Methods in Medical Research
|May 14, 2020
PubMed
Summary
This summary is machine-generated.

Shrinkage methods improve risk prediction model performance on average, but increase variability and can perform poorly on individual datasets, especially with small sample sizes or few events per variable.

Keywords:
Clinical risk prediction modelsFirth’s correctionlogistic regressionmaximum likelihoodpenalized likelihoodshrinkage

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Area of Science:

  • Statistical modeling
  • Biostatistics
  • Machine learning

Background:

  • Shrinkage methods are recommended for risk prediction models with limited sample sizes.
  • Previous research indicates shrinkage generally enhances predictive performance.

Purpose of the Study:

  • To investigate the variability of regression shrinkage on predictive performance for binary outcomes.
  • To compare standard maximum likelihood with various shrinkage techniques.

Main Methods:

  • Simulation study varying predictor count, strength, correlation, event rate, and events per variable.
  • Comparison of maximum likelihood with uniform shrinkage, penalized maximum likelihood (ridge), LASSO, adaptive LASSO, and Firth's correction.
  • Focus on calibration slope to assess risk prediction extremity.

Main Results:

  • Shrinkage improved calibration slopes on average.
  • Between-sample variability of calibration slopes often increased with shrinkage methods compared to maximum likelihood.
  • Firth's correction showed minimal shrinkage effect and low variability.
  • Negative correlation between estimated and optimal shrinkage was common, except for Firth's correction.

Conclusions:

  • Shrinkage methods enhance average predictive performance but can perform poorly in individual datasets, particularly when sample size or events per variable are low.
  • Shrinkage does not fully resolve issues related to small sample sizes or low event counts in risk prediction models.