Bayesian model averaging for randomized dose optimization trials in multiple indications

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.