Drug Screening: Simulation Based Sequential Design

描述（由申请人提供）：在致力于癌症临床研究的大型机构中，大量新药或抗癌药物的新组合进行活性评估。这个过程通常是通过单独的II期研究进行的，在研究之间只进行非正式的学习——即使这些研究吸引了具有相似疾病特征的患者。有必要对整个二期试验或筛选过程采取更系统、更合理的方法，以便更有效地设计研究，更多地了解癌症治疗以及哪些有效，哪些无效。该申请描述了基于Yao、Begg和Livingston最初提出的抗癌疫苗评估工作的研究，在这个过程中，整个过程被认为是一个大型企业，在这个企业中，通过某种机制引入的多种药物经过活性筛选，要么进行进一步的测试，要么被丢弃。通过结合药物特异性共同元素的数学模型，在新药之间共享尽可能多的信息，本申请中提出的研究设想将这一过程从一个面向疫苗试验的过程扩展到大型生物医学研究中心存在的更大的二期测试项目。最终目标是尽可能多地了解药物和患者，从而最大限度地提高每个患者受益的机会，同时将有害副作用的风险降到最低。

DESCRIPTION (provided by applicant): At large institutions dedicated to clinical research in cancer a large number of new agents or new combinations of anticancer agents undergo evaluation for activity. The process is typically carried out through separate phase II studies with only informal learning carried out between studies--even if the studies draw patients with similar disease characteristics. There is a need for a more systematic and rational approach to the whole phase II testing or screening process to allow for more efficient study design and greater learning about cancer treatment and what works or does not work. This application describes research building on work first proposed by Yao, Begg, and Livingston for evaluating anti-cancer vaccines, in which the entire process is considered a large enterprise within which multiple agents, introduced by some mechanism, undergo screening for activity and either progress to further testing or are discarded. By sharing as much information as can be shared between new agents via mathematical models that incorporate drug-specific common elements, research proposed in this application envisions broadening this process from one geared to vaccine trials to the larger phase II testing programs that exist at large biomedical research centers. The ultimate goal is to learn as much as possible about the agents and the patients, so as to maximize each patient's chances of benefit while minimizing the risk of detrimental side effects. Research in the initial pilot phase (R21) will provide a proof of concept for strategies to overcome the prohibitive computational challenges involved in carrying out a decision theoretic solution to the problem. The first specific aim develops a simulation based approach for sequential design. The second specific aim applies the developed algorithm in a highly stylized version of the drug screening problem. Milestones are set to define the targeted research goals in an easily verifiable manner. Research in the following extended development phase (R33) targets three specific aims. The first specific aim proposes to develop non-sequential policies to solve the sequential decision problem of evaluating an sequence of phase II trials. Policies are defined in terms of fixed decision boundaries, allowing optimization up-front, without the need for backward induction. Finding the optimal policy is a challenging high dimensional stochastic optimization problem. The second specific aim is the construction of new hybrid algorithms which combine the parsimony and robustness of non-sequential policies with the flexibility of unconstrained sequential solutions by dynamic programming. The third specific aim targets the extensions of the underlying probability model needed to accommodate a realistic application to continuous drug screening. These extensions pose challenging research problems related to modeling ordinal responses. multiple and delayed outcomes. and repeated longitudinal measurements.