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Leveraging external information by guided adaptive shrinkage to improve variable selection in high-dimensional

Mark A van de Wiel1, Wessel N van Wieringen1,2

  • 1Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands.

The International Journal of Biostatistics
|September 17, 2025
PubMed
Summary

Guided adaptive shrinkage methods leverage external co-data to enhance variable selection in high-dimensional, low-sample-size settings. This approach improves prediction accuracy by adapting shrinkage parameters using complementary information, particularly in genomics.

Keywords:
penalized regressionprior informationshrinkagevariable selection

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Area of Science:

  • Statistics
  • Bioinformatics
  • Machine Learning

Background:

  • High-dimensional data with low sample sizes presents significant variable selection challenges.
  • External information, termed 'co-data', can improve variable selection accuracy.
  • Co-data, such as variable groupings or prior p-values, is abundant in genomics.

Purpose of the Study:

  • To review guided adaptive shrinkage methods that utilize co-data for improved variable selection.
  • To discuss the technical aspects and applicability of co-data in prediction models.
  • To compare guided shrinkage with other methods like sparse group-lasso.

Main Methods:

  • Review of guided adaptive shrinkage methods.
  • Adaptation of shrinkage parameters using co-data.
  • Comparison with sparse group-lasso for variable selection.
  • Integration of co-data learners and spike-and-slab priors for 'do-it-yourself' implementation.

Main Results:

  • Guided adaptive shrinkage methods effectively use co-data to enhance variable selection.
  • The methodology demonstrates versatility in integrating different co-data types.
  • Demonstration of improved variable selection in genetics studies through DIY implementation.

Conclusions:

  • Guided adaptive shrinkage offers a powerful framework for variable selection with co-data.
  • The methods are applicable across various domains, especially genomics.
  • Practical implementation guidance is provided for researchers.