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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Related Experiment Video

Updated: Jun 6, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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DOD-BART: machine learning-based dose optimization design incorporating patient-level prognostic factors via Bayesian

Yunqi Zhao1, Rachael Liu1, Jianchang Lin1

  • 1Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA.

Journal of Biopharmaceutical Statistics
|November 29, 2024
PubMed
Summary

This study introduces a novel seamless phase I/II clinical trial design, DOD-BART, using Bayesian Additive Regression Trees (BART) for improved oncology drug dose optimization. The design enhances efficiency and accuracy in identifying optimal doses by integrating patient data and adapting to emerging outcomes.

Keywords:
Bayesian additive regression treesBayesian automated adaptive designdose optimizationdose randomizationheterogeneitymachine learningprognostic factors

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Area of Science:

  • Oncology
  • Clinical Trial Design
  • Biostatistics

Background:

  • Dose optimization is crucial but challenging in early-phase oncology trials due to heterogeneity and uncertainties.
  • Gaps between early phase and Phase 3 trials can increase drug development failure rates.
  • Existing methods struggle to fully incorporate patient-level factors for effective dose selection.

Purpose of the Study:

  • To propose a novel seamless phase I/II clinical trial design, DOD-BART, for efficient dose optimization.
  • To leverage machine learning, specifically Bayesian Additive Regression Trees (BART), for enhanced dose selection.
  • To address challenges in early-phase trials by integrating patient prognostic factors and outcomes.

Main Methods:

  • Developed the DOD-BART (Dose Optimization Design with Bayesian Additive Regression Trees) seamless phase I/II design.
  • Utilized BART to incorporate patient-level prognostic factors and outcomes dynamically.
  • Simulated trial scenarios to evaluate design performance across various settings.

Main Results:

  • DOD-BART demonstrated robust performance in simulations.
  • The design showed high probabilities of correctly identifying optimal doses.
  • Patients were effectively allocated to more tolerable and efficacious dose levels, with efficient data utilization.

Conclusions:

  • The DOD-BART design offers a streamlined and data-driven approach to dose exploration and optimization in oncology.
  • This novel design improves operational efficiency and reduces bias in dose estimation.
  • DOD-BART enhances the probability of successful drug development by bridging early-phase uncertainties and later-stage trial requirements.