CAREER: Innovative Methods for Designing Adaptive Clinical Trials

该学院早期职业发展(CALEAR)项目的目标是为临床试验适应性设计中的最佳学习开发一个灵活的优化框架。自2004年联邦药物管理局(FDA)批准这一概念并开始试点研究以来，适应性设计一直引起人们的极大兴趣。与固定临床试验不同，在固定临床试验中，样本量是预先确定的，只有在试验结束时才能得出结论，适应性临床试验允许在将患者分配到治疗组、调整剂量水平或根据正在形成的成功或失败证据终止试验方面进行临时修改。适应性试验有望提高新疗法的安全性，缩短有效疗法的上市时间，并限制接触劣质疗法。此外，适应性临床试验可以更容易地纳入患者的异质性(例如，基于生物标记物或个人暴露)，这可能导致针对特定患者亚组的更有效的个性化治疗。教育计划将在本科生和研究生课程中纳入适应性设计概念，作为引入最佳学习技术的新手段。此外，该项目将为通过两个合作的医疗系统进行临床试验的医生、护士和其他医疗从业者提供关于适应方法的教育研讨会。这个职业项目将通过建立两类最优学习问题的统一解决方案框架来促进知识的发展，这两类问题是：(I)具有任意(可能相关的)信念分布的排名和选择问题，以及(Ii)具有相关回报和学习最佳人群和最大化总回报的双重目标的多臂匪徒问题，其中在每个阶段，任何武器子集可以被选择任意次数，但受关于拉动总次数的预算的限制。采用新的近似动态规划方法和贝叶斯统计相结合的方法来研究解空间，并基于对偶理论建立了评价解质量的界。这一结果将阐明患者异质性在适应性临床试验设计中所起的作用，并应扩展到最优学习的其他应用，如动态定价、收入管理和分类规划。

The goal of this Faculty Early Career Development (CAREER) project is to develop a flexible optimization framework for optimal learning in adaptive design of clinical trials. Adaptive designs have been of great interest since the Federal Drug Administration (FDA) approved the concept and initiated pilot studies in 2004. Unlike fixed clinical trials, where sample sizes are determined in advance and conclusions made only at the end of the trial, adaptive clinical trials allow for interim modifications in assigning patients to treatment groups, adjusting of dosage levels, or terminating trials based on developing evidence of success or failure. Adaptive trials hold the promise of improving safety of new treatments, reducing time to market of efficacious treatments, and limiting exposure to inferior treatments. Moreover, adaptive clinical trials can more easily incorporate patient heterogeneity (e.g., based on biomarkers or individual exposure), which may lead to more effective, personalized treatments for particular patient subsets. The educational plan will incorporate adaptive design concepts in undergraduate and graduate coursework as a novel means of introducing optimal learning techniques. In addition, the project will provide educational seminars on adaptive methods to physicians, nurses, and other healthcare practitioners who are conducting clinical trials through two collaborating healthcare systems. This CAREER project will advance knowledge by establishing unifying solution frameworks to two classes of optimal learning problems, namely (i) ranking and selection problems with arbitrary (possibly correlated) belief distribution and the objective of learning a population with a desired property, and (ii) multiarmed bandit problems with correlated rewards and the dual objective of learning the best population and maximizing the total reward, where at each period any subset of arms can be chosen any number of times subject to a budget on the total number of pulls. Novel approximate dynamic programming methods integrated with Bayesian statistics are employed to study the solution space and establish bounds based on duality theory that will assess the quality of solutions. The results will shed light on the role that patient heterogeneity plays in adaptive clinical trial design and should extend to other applications in optimal learning, such as dynamic pricing, revenue management, and assortment planning.