Early-Stage Clinical Trials with Patient Choice

该项目研究为早期临床试验设计有效机制的数学方法。早期临床试验旨在为随后的临床试验阶段确定研究药物的安全和治疗剂量。由于事先对研究药物的毒性谱知之甚少，目前的早期试验方法采用剂量逐步递增的方法，即连续几组患者接受不断增加的剂量，直到观察到剂量限制的毒性。这种做法往往导致在招募和留住患者方面面临重大挑战，因为许多患者认为参与的好处很低。例如，已用尽所有其他选择的绝症患者通常自愿参加早期试验，在早期试验中，开出的剂量低且不具治疗性，而相对健康的患者可能会推迟参与，以等待有关剂量安全性和有效性的更多信息。过于保守的剂量递增方法往往会导致大量的试验延迟和费用，而对患者的安全性几乎没有好处。该项目研究了根据个体患者风险偏好允许有限的患者输入以指导剂量决定对试验安全性和有效性的影响。PI旨在通过课程作业和研究支持，让本科生(通过克莱姆森的创造性探究学习计划)和研究生参与研究。该项目引入了一类新的动态剂量选择游戏，带有学习功能，将不断变化的患者偏好和对剂量效率和剂量毒性的信念与最佳停止规则相结合，以确定证据何时收敛于可接受的剂量，以转移到以后的试验。该研究计划采用了一种伪市场机制，该机制解决了通过将集中式剂量分配决策委托给自利的代理人(患者及其医生)而创建的一种新的探索和效率权衡，而中央当局则评估研究数据的结果。研究了解决分配效率(最大化当前参与者的效用)和探索(最大化总体效用)之间的潜在冲突的方法，从而最大化学习，统一探索剂量范围，并避免集中在小剂量范围内。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project investigates mathematical approaches for designing efficient mechanisms for early-stage clinical trials. Early-stage clinical trials aim to establish safe and therapeutic doses of an investigational drug for subsequent clinical trial phases. Because little is known in advance about the toxicity profile of investigational drugs, current approaches to early-stage trials employ dose-escalation methods, in which consecutive groups of patients receive increasing doses until dose-limiting toxicities are observed. This practice often results in significant challenges in recruiting and retaining patients, as many patients perceive the benefits of participation to be low. For example, terminally ill patients who have exhausted all other options often volunteer for early trials, in which the doses prescribed are low and nontherapeutic, whereas relatively healthy patients may postpone their involvement to wait for more information on dosing safety and efficacy. Overly conservative dose-escalation methods often result in substantial trial delays and expense, with little gain in patient safety. This project studies the effect on trial safety and efficacy of allowing limited patient input based on individual patient risk preferences to guide dosage decisions. The PIs aim to involve both undergraduate students (through Clemson's Creative Inquiry learning program) and graduate students in the research, through both coursework and research support. The project introduces a new class of dynamic dose-selection games with learning that integrate evolving patient preferences and beliefs about dose efficiency and dose toxicity with optimal stopping rules to determine when evidence converges on acceptable dosage to move to later trials. The research plan employs a pseudo-market mechanism that addresses a novel exploration-and-efficiency tradeoff created by delegating the centralized dose allocation decision to self-interested agents (patients and their physicians), while the central authority evaluates the results of study data. Methods are investigated to resolve potential conflicts between allocation efficiency (maximizing current participants’ utility) and exploration (maximizing the general population utility) so as to maximize learning, explore the dose-range uniformly, and avoid concentration within a small dosage range.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.