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Bayesian adaptive model selection design for optimal biological dose finding in phase I/II clinical trials
Ruitao Lin1, Guosheng Yin2, Haolun Shi3
1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA.
Biostatistics (Oxford, England)
|July 23, 2021
Summary
Finding the optimal drug dose is challenging. This study introduces a new adaptive design for phase I/II trials that uses Bayesian model selection to simultaneously consider toxicity and efficacy, improving dose accuracy.
Area of Science:
- * Oncology
- * Clinical Pharmacology
- * Biostatistics
Background:
- * Identifying the optimal biological dose (OBD) is a critical challenge in developing novel therapeutics, including molecularly targeted agents, immunotherapies, and CAR T-cell therapies.
- * Existing dose-finding methods often struggle with simultaneously incorporating both efficacy and toxicity data, especially for treatments with delayed outcomes.
Purpose of the Study:
- * To propose a novel adaptive trial design for phase I/II clinical trials that efficiently and accurately identifies the optimal biological dose (OBD).
- * To develop a flexible, curve-free Bayesian model selection framework that integrates toxicity and efficacy outcomes without parametric assumptions.
- * To extend the design to handle late-onset outcomes common in immunotherapies.
Main Methods:
- * A Bayesian model selection approach is employed, treating dose-finding as a problem of selecting the best model from a set of possibilities.
- * Curve-free models are specified for both toxicity and efficacy endpoints, allowing for flexible dose-response relationships.
- * The design adaptively assigns doses based on integrated data across all levels, ensuring coherence and enhancing efficiency.
Main Results:
- * Extensive simulation studies demonstrate satisfactory and robust performance of the proposed design.
- * The design exhibits desirable coherence properties, outperforming most existing phase I/II designs.
- * The methodology was successfully applied and exemplified in a phase I/II clinical trial for chronic lymphocytic leukemia.
Conclusions:
- * The proposed Bayesian adaptive design offers a novel, flexible, and coherent framework for optimal biological dose determination in phase I/II trials.
- * This approach enhances the efficiency and accuracy of dose selection, particularly for complex therapies and late-onset outcomes.
- * The design provides a significant advancement in clinical trial methodology for optimizing cancer therapeutics and other treatments.
Keywords:
Bayesian adaptive designBayesian model selectionDelayed responseOptimal biological dosePhase I/II trialMore Related Videos
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