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Bayesian model averaging for randomized dose optimization trials in multiple indications
1Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Abstract:
In oncology dose-finding trials, small cohorts of patients are often assigned to increasing dose levels, with the aim of determining the maximum tolerated dose. In the era of targeted agents, this practice has come under intense scrutiny as treating patients at doses beyond a certain level often results in increased off-target toxicity without significant gains in antitumor activity. Dose optimization for targeted agents becomes more challenging in proof-of-concept trials when the experimental treatment is tested in multiple indications of low prevalence and there is the need to characterize the dose-response relationship in each indication. To provide an alternative to the conventional "more is better" paradigm in oncology dose finding, we propose a Bayesian model averaging approach based on robust mixture priors (rBMA) for identifying the recommended phase III dose in randomized dose optimization studies conducted simultaneously in multiple indications. Compared to the dose optimization strategy which evaluates the dose-response relationship in each indication independently, we demonstrate the proposed approach can improve the accuracy of dose recommendation by learning across indications. The performance of the proposed approach in making the correct dose recommendation is examined based on systematic simulation studies.
Insights
This study introduces a Bayesian model averaging approach for oncology dose-finding trials. This method improves dose recommendation accuracy by learning across multiple indications, offering an alternative to traditional methods.
Area of Science:
- Oncology
- Clinical Trial Design
- Biostatistics
Background:
- Conventional oncology dose-finding trials face challenges with targeted agents, often leading to toxicity without improved efficacy.
- Optimizing doses for targeted agents in multi-indication proof-of-concept trials is complex due to low prevalence and the need for indication-specific dose-response characterization.
Purpose of the Study:
- To propose a novel Bayesian model averaging approach using robust mixture priors (rBMA).
- To identify the recommended Phase III dose in randomized dose optimization studies conducted simultaneously across multiple indications.
- To offer an alternative to the "more is better" paradigm in oncology dose finding.
Main Methods:
- Developed a Bayesian model averaging approach with robust mixture priors (rBMA).
- Applied the approach to randomized dose optimization studies conducted simultaneously in multiple indications.
- Conducted systematic simulation studies to evaluate performance.
Main Results:
- The proposed rBMA approach improves the accuracy of dose recommendations compared to independent indication-specific strategies.
- The model effectively learns across indications, enhancing dose-finding precision.
- Simulation studies confirmed the approach's performance in making correct dose recommendations.
Conclusions:
- The rBMA approach provides a more accurate and efficient method for dose optimization in multi-indication oncology trials.
- This Bayesian strategy offers a viable alternative to conventional dose-finding paradigms for targeted agents.
- Cross-indication learning enhances the reliability of recommended Phase III doses.
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