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Bayesian adaptive trial design for a continuous biomarker with possibly nonlinear or nonmonotone prognostic or
Yusha Liu1, John A Kairalla2, Lindsay A Renfro3
1Department of Human Genetics, University of Chicago, Chicago, Illinois, USA.
Biometrics
|August 20, 2021
Summary
This study introduces a new clinical trial design for continuous biomarkers, improving adaptive enrichment strategies. It accurately models complex biomarker relationships for better patient subgroup identification and treatment decisions.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Translational Oncology
Background:
- Biomarker-driven clinical trials are crucial for personalized medicine.
- Adaptive enrichment designs enhance efficiency but often oversimplify continuous biomarkers.
Purpose of the Study:
- To propose a novel adaptive trial design accommodating continuous biomarkers with complex relationships.
- To compare the performance of the proposed design against traditional threshold-based methods.
Main Methods:
- Developed an adaptive decision-making framework using continuous marker effects and posterior uncertainty.
- Employed simulations and real-world patient data from an Acute Lymphoblastic Leukemia trial.
Main Results:
- The proposed design effectively utilizes the shape of continuous marker effects, unlike traditional dichotomization.
- Demonstrated competitive operating characteristics compared to standard approaches in simulations and trial data.
Conclusions:
- This novel design offers a more flexible and accurate approach for biomarker-driven adaptive clinical trials.
- It enables better identification of patient subpopulations with differential treatment benefits, especially with non-linear biomarker effects.
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