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，对大量新药或抗癌药的新组合进行活性评价。这一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II期测试或筛查过程采取更系统和合理的方法，以实现更有效的研究设计，并更多地了解癌症治疗以及哪些有效或无效。本申请描述了基于Yao、Begg和利文斯顿首次提出的用于评估抗癌疫苗的工作的研究，其中整个过程被认为是一个大型企业，其中通过某种机制引入的多种药剂进行活性筛选，并进行进一步测试或被丢弃。通过共享尽可能多的信息，可以通过合并药物特异性共同元素的数学模型在新代理之间共享，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扩展到大型生物医学研究中心存在的更大的II期测试程序。最终目标是尽可能多地了解药物和患者，以便最大限度地提高每个患者的获益机会，同时最大限度地降低有害副作用的风险。 在最初的试点阶段（R21）的研究将提供一个概念证明的战略，以克服在进行决策理论的解决方案的问题所涉及的禁止计算的挑战。第一个具体目标是开发一种基于仿真的顺序设计方法。第二个具体目标是将所开发的算法应用于药物筛选问题的高度程式化版本。Milestones被设置为以一种容易验证的方式定义有针对性的研究目标。 下一个扩展开发阶段（R33）的研究针对三个具体目标。第一个具体目标提出了制定非顺序的政策，以解决顺序决策问题，评估一个序列的第二阶段试验。策略是根据固定的决策边界定义的，允许预先优化，而不需要向后归纳。寻找最优策略是一个具有挑战性的高维随机优化问题。第二个具体的目标是建设新的混合算法相联合收割机的简约性和鲁棒性的非顺序政策的灵活性，无约束的顺序解决方案，通过动态规划。第三个具体目标是扩展潜在的概率模型，以适应连续药物筛选的实际应用。这些扩展提出了与有序响应建模相关的具有挑战性的研究问题。多个和延迟的结果。和重复的纵向测量。

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.