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Nonparametric competing risks analysis using Bayesian Additive Regression Trees.

Rodney Sparapani1, Brent R Logan1, Robert E McCulloch2

  • 1Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, USA.

Statistical Methods in Medical Research
|January 8, 2019
PubMed
Summary
This summary is machine-generated.

Bayesian Additive Regression Trees (BART) offer a flexible approach for analyzing competing risks data, improving prediction accuracy in complex scenarios. This method outperforms standard techniques for time-to-event analysis with multiple outcomes.

Keywords:
Cumulative incidencegraft-versus-host disease (GVHD)hematopoietic stem cell transplantmachine learningnonproportionaltreatment heterogeneityvariable selection

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Survival Analysis

Background:

  • Competing risks complicate time-to-event studies, often analyzed with Cox or Fine and Gray models.
  • Standard models struggle with complex relationships like nonlinearities, interactions, and nonproportional hazards.
  • Model misspecification in competing risks analysis can compromise predictive performance.

Purpose of the Study:

  • To introduce a novel flexible prediction modeling approach for competing risks data.
  • To evaluate the performance of Bayesian Additive Regression Trees (BART) for competing risks.
  • To compare BART against established regression techniques and random survival forests.

Main Methods:

  • Utilized Bayesian Additive Regression Trees (BART) for flexible prediction modeling.
  • Assessed simulation performance in two-sample and complex regression settings.
  • Benchmarked BART against Cox models, Fine and Gray models, and random survival forests.
  • Applied the method to a real-world dataset from hematopoietic stem cell transplantation.

Main Results:

  • BART demonstrated robust performance in complex regression settings for competing risks.
  • The proposed method showed competitive or superior predictive performance compared to standard techniques.
  • Simulation studies confirmed the effectiveness of BART in handling intricate covariate relationships.

Conclusions:

  • Bayesian Additive Regression Trees (BART) provide a powerful and flexible tool for competing risks data analysis.
  • This approach enhances predictive accuracy, particularly when standard models face challenges.
  • BART offers a valuable alternative for researchers analyzing complex time-to-event data with competing risks.