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Bayesian Additive Regression Trees (BART) with covariate adjusted borrowing in subgroup analyses.

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Journal of Biopharmaceutical Statistics
|June 23, 2022
PubMed
Summary

Bayesian Additive Regression Trees (BART) offers a data-driven approach for clinical trial subgroup analyses. This machine learning method objectively determines information borrowing between subgroups, improving drug development objectivity.

Keywords:
Bayesian Additive Regression TreesBayesian semiparametricbasket trialsindividual patient datamachine learningsubgroup borrowing

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Machine Learning

Background:

  • Investigating treatment effect consistency across patient subgroups is vital in clinical trials.
  • Small subgroup sample sizes can lead to treatment effect variability, necessitating methods for borrowing information across subgroups.
  • Existing Bayesian models for subgroup analysis rely on subjective assumptions of homogeneity between patient groups.

Purpose of the Study:

  • To propose a novel method for subgroup analysis in clinical trials using Bayesian Additive Regression Trees (BART).
  • To overcome the limitations of existing models by removing the assumption of subgroup homogeneity.
  • To provide an objective, data-driven approach for determining information borrowing between subgroups.

Main Methods:

  • Utilized Bayesian Additive Regression Trees (BART), a machine learning technique.
  • Modeled patient-level data to learn covariate-response relationships automatically.
  • Compared BART performance against existing subgroup borrowing methods via simulation and a non-small cell lung cancer case study.

Main Results:

  • BART provides a data-driven method for subgroup borrowing without assuming homogeneity.
  • The amount of borrowing is automatically adjusted based on learned covariate-response relationships.
  • BART demonstrated comparable or improved performance in simulation and case studies.

Conclusions:

  • Bayesian Additive Regression Trees (BART) offers an objective approach to subgroup analysis in clinical trials.
  • This method alleviates the need for subjective decisions on information borrowing based on subgroup similarity.
  • BART enhances objective decision-making in drug development and is applicable to basket trials.