A flexible design for advanced Phase I/II clinical trials with continuous efficacy endpoints

Bayesian dose escalation with overdose and underdose control utilizing all toxicities in Phase I/II clinical trials

Escalation with overdose control (EWOC) is a commonly used Bayesian adaptive design, which controls overdosing risk while estimating maximum tolerated dose (MTD) in cancer Phase I clinical trials. In 2010, Chen and his colleagues proposed a novel toxicity scoring system to fully utilize patients' toxicity information by using a normalized equivalent toxicity score (NETS) in the range 0 to 1 instead of a binary indicator of dose limiting toxicity (DLT). Later in 2015, by adding underdosing control into EWOC, escalation with overdose and underdose control (EWOUC) design was proposed to guarantee patients the minimum therapeutic effect of drug in Phase I/II clinical trials. In this paper, the EWOUC-NETS design is developed by integrating the advantages of EWOUC and NETS in a Bayesian context. Moreover, both toxicity response and efficacy are treated as continuous variables to maximize trial efficiency. The dose escalation decision is based on the posterior distribution of both toxicity and efficacy outcomes, which are recursively updated with accumulated data. We compare the operation characteristics of EWOUC-NETS and existing methods through simulation studies under five scenarios. The study results show that EWOUC-NETS design treating toxicity and efficacy outcomes as continuous variables can increase accuracy in identifying the optimized utility dose (OUD) and provide better therapeutic effects.

A Bayesian design for finding optimal biological dose with mixed types of responses of toxicity and efficacy

For molecularly targeted therapy and immunotherapy, the targeted dose in the early phase clinical trial has been shifted from the maximum tolerated dose for the cytotoxic drug to the optimal biological dose where both toxicity and efficacy are considered. In this paper, we consider the situation that the responses of toxicity and efficacy are mixed in binary and continuous types, respectively, where the continuous endpoint bears more magnitude information than the binary endpoint after dichotomization. We propose combining two model-based designs to sequentially identify the most efficacious and tolerably safe dose. The employed designs both take the dose level information into account to achieve high estimation efficiency. We demonstrate the superiority of the proposed method to some existing methods by simulation.

A flexible design for advanced Phase I/II clinical trials with continuous efficacy endpoints

There is growing interest in integrated Phase I/II oncology clinical trials involving molecularly targeted agents (MTA). One of the main challenges of these trials are nontrivial dose-efficacy relationships and administration of MTAs in combination with other agents. While some designs were recently proposed for such Phase I/II trials, the majority of them consider the case of binary toxicity and efficacy endpoints only. At the same time, a continuous efficacy endpoint can carry more information about the agent's mechanism of action, but corresponding designs have received very limited attention in the literature. In this work, an extension of a recently developed information-theoretic design for the case of a continuous efficacy endpoint is proposed. The design transforms the continuous outcome using the logistic transformation and uses an information-theoretic argument to govern selection during the trial. The performance of the design is investigated in settings of single-agent and dual-agent trials. It is found that the novel design leads to substantial improvements in operating characteristics compared to a model-based alternative under scenarios with nonmonotonic dose/combination-efficacy relationships. The robustness of the design to missing/delayed efficacy responses and to the correlation in toxicity and efficacy endpoints is also investigated.