  Phase I dose-finding trials are increasingly challenging as the relationship
between efficacy and toxicity of new compounds (or combination of them) becomes
more complex. Despite this, most commonly used methods in practice focus on
identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity
events. We present a novel adaptive clinical trial methodology, called Safe
Efficacy Exploration Dose Allocation (SEEDA), that aims at maximizing the
cumulative efficacies while satisfying the toxicity safety constraint with high
probability. We evaluate performance objectives that have operational meanings
in practical clinical trials, including cumulative efficacy,
recommendation/allocation success probabilities, toxicity violation
probability, and sample efficiency. An extended SEEDA-Plateau algorithm that is
tailored for the increase-then-plateau efficacy behavior of molecularly
targeted agents (MTA) is also presented. Through numerical experiments using
both synthetic and real-world datasets, we show that SEEDA outperforms
state-of-the-art clinical trial designs by finding the optimal dose with higher
success rate and fewer patients.
