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Constrained hierarchical Bayesian model for latent subgroups in basket trials with two classifiers.

Kentaro Takeda1, Shufang Liu1, Alan Rong1

  • 1Data Science, Astellas Pharma Global Development, Inc., Northbrook, Illinois, USA.

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Summary

This study introduces a new Bayesian model for oncology basket trials to handle varying treatment effects across cancer types and biomarkers. The constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) improves statistical power and controls errors in heterogeneous treatment effect scenarios.

Keywords:
basket trialsclassifierconstrained hierarchical Bayesian modelheterogeneitylatent subgroup

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Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Basket trials assess one treatment across multiple cancer types.
  • Heterogeneity in treatment effects arises from multiple classifiers (e.g., cancer type, biomarkers).
  • The assumption of exchangeability is often violated, complicating analysis.

Purpose of the Study:

  • To propose a novel constrained hierarchical Bayesian model for latent subgroups (CHBM-LS).
  • To address potential heterogeneity of treatment effects in basket trials with multiple classifiers.
  • To enable adaptive information borrowing across baskets by identifying latent subgroups.

Main Methods:

  • Developed a constrained hierarchical Bayesian model for latent subgroups (CHBM-LS).
  • Aggregated baskets into latent subgroups using a latent subgroup modeling approach.
  • Evaluated treatment effects within each latent subgroup, assuming approximate exchangeability.

Main Results:

  • The CHBM-LS approach effectively handles heterogeneity in treatment effects across baskets.
  • Simulation studies demonstrated superior performance compared to other methods.
  • CHBM-LS achieved higher statistical power and better control of type I error rates.

Conclusions:

  • CHBM-LS provides a robust statistical framework for analyzing complex oncology basket trials.
  • The model facilitates adaptive information borrowing, enhancing analytical power.
  • This approach is valuable for trials with heterogeneous treatment effects influenced by multiple patient or disease characteristics.