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Adaptive Use of Co-Data Through Empirical Bayes for Bayesian Additive Regression Trees.

Jeroen M Goedhart1, Thomas Klausch1, Jurriaan Janssen2

  • 1Department of Epidemiology & Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers Location AMC, Noord Holland, The Netherlands.

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Summary

This study introduces an empirical Bayes framework to enhance Bayesian Additive Regression Trees (BART) for clinical prediction with small datasets. The method effectively identifies relevant covariates and improves predictive accuracy, especially for complex relationships.

Keywords:
Bayesian additive regression treesco‐dataempirical Bayeshigh‐dimensional dataomicsprediction

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Area of Science:

  • Biostatistics
  • Machine Learning
  • Genomics

Background:

  • Clinical prediction models often struggle with small sample sizes and numerous covariates.
  • Complex covariate-response relationships further challenge variable selection and prediction accuracy.

Purpose of the Study:

  • To propose a novel empirical Bayes (EB) framework for incorporating external covariate information into Bayesian Additive Regression Trees (BART).
  • To improve variable selection and prediction accuracy in small sample size clinical datasets.

Main Methods:

  • Developed an empirical Bayes (EB) framework to estimate prior covariate weights within the BART model.
  • The EB framework also estimates other BART prior parameters, offering an alternative to cross-validation.
  • Applied the method to diffuse large B-cell lymphoma (DLBCL) prognosis data.

Main Results:

  • The proposed EB-BART method successfully identifies relevant covariates.
  • It demonstrates improved prediction accuracy compared to default BART in simulation studies.
  • The method outperforms regression-based learners, particularly for non-linear covariate-response relationships.

Conclusions:

  • Incorporating external covariate information via the EB-BART framework enhances predictive performance in clinical settings.
  • This approach provides a computationally efficient and effective alternative for complex prediction tasks with limited data.
  • The utility is demonstrated in predicting DLBCL prognosis using multi-omics data.