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gBOIN-ET: The generalized Bayesian optimal interval design for optimal dose-finding accounting for ordinal graded

Kentaro Takeda1, Satoshi Morita2, Masataka Taguri3

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

Biometrical Journal. Biometrische Zeitschrift
|May 13, 2022
PubMed

Insights

A new generalized Bayesian optimal interval design (gBOIN-ET) efficiently identifies optimal doses for novel oncology therapies by considering both efficacy and toxicity. This approach improves dose selection and patient allocation in clinical trials.

Area of Science:

  • Oncology
  • Clinical Trial Design
  • Biostatistics

Background:

  • Identifying optimal doses (OD) for novel molecular targeted and immune-oncology agents is crucial for subsequent clinical trials.
  • New therapies often present with multiple low-to-moderate grade toxicities rather than severe dose-limiting ones.
  • Efficacy assessment in oncology trials requires nuanced evaluation of response categories.

Purpose of the Study:

  • To propose a novel dose-finding design that integrates both efficacy and toxicity grades for oncology trials.
  • To introduce the generalized Bayesian optimal interval (gBOIN-ET) design as a practical alternative to complex model-based methods.

Main Methods:

  • The study introduces the gBOIN-ET design, a model-assisted approach for oncology dose-finding.
  • This design accounts for various grades of toxicity and efficacy outcomes.
  • Implementation is designed to be simple and straightforward for practical clinical use.

Main Results:

  • Simulation studies demonstrate the gBOIN-ET design's superiority over existing model-assisted designs.
  • The gBOIN-ET design shows improved percentage of correct optimal dose selection.
  • It also demonstrates advantages in the average number of patients allocated to optimal doses across diverse scenarios.

Conclusions:

  • The gBOIN-ET design offers a valuable, practical, and effective method for oncology dose-finding.
  • It enhances the precision of identifying optimal doses for novel cancer therapeutics.
  • This design facilitates more efficient patient allocation in early-phase oncology trials.
Keywords:
Bayesian adaptive dose-finding designefficacy grademodel-assisted designphase I-II clinical trial designtoxicity grade

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