| Popis: |
Phase I dose-finding trials are increasingly chal- lenging 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 fo- cus on identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity events. We present a novel adaptive clinical trial methodol- ogy, called Safe Efficacy Exploration Dose Al- location (SEEDA), that aims at maximizing the cumulative efficacies while satisfying the toxicity safety constraint with high probability. We evalu- ate performance objectives that have operational meanings in practical clinical trials, including cumulative efficacy, recommendation/allocation success probabilities, toxicity violation probabil- ity, and sample efficiency. An extended SEEDA- Plateau algorithm that is tailored for the increase- then-plateau efficacy behavior of molecularly tar- geted agents (MTA) is also presented. Through numerical experiments using both synthetic and real-world datasets, we show that SEEDA out- performs state-of-the-art clinical trial designs by finding the optimal dose with higher success rate and fewer patients. |
