Drug Screening: Simulation Based Sequential Design

描述(申请人提供)：在致力于癌症临床研究的大型机构，大量新的抗癌药物或新的抗癌药物组合正在接受活性评估。这一过程通常通过单独的第二阶段研究进行，只在研究之间进行非正式学习--即使这些研究吸引了具有相似疾病特征的患者。需要对整个第二阶段测试或筛查过程采取更系统和更合理的方法，以便更有效地进行研究设计，并更好地了解癌症治疗以及哪些有效或哪些无效。本申请描述了在姚、贝格和利文斯顿首次提出的抗癌疫苗评估工作的基础上进行的研究，在该工作中，整个过程被认为是一个大型企业，在该企业中，通过某种机制引入的多种药物进行活性筛选，并要么进行进一步测试，要么被丢弃。通过包含药物特定共同元素的数学模型，在新代理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面向疫苗试验的过程扩大到大型生物医学研究中心存在的更大的II期测试计划。最终目标是尽可能多地了解药物和患者，以便最大限度地增加每个患者的受益机会，同时将有害副作用的风险降至最低。 初步试点阶段(R21)的研究将为战略提供概念证明，以克服在对问题进行决策理论解决方案时涉及的令人望而却步的计算挑战。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开发的算法应用于高度程式化的药物筛选问题。里程碑被设定为以一种容易核实的方式定义目标研究目标。 在接下来的扩展开发阶段(R33)中的研究以三个具体目标为目标。第一个具体目标是开发非序贯策略来解决评估II期试验序列的序贯决策问题。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极具挑战性的高维随机优化问题。第二个具体目标是构造新的混合算法，它结合了非顺序策略的简洁性和健壮性，以及动态规划无约束顺序解的灵活性。第三个具体目标是扩展基本概率模型，以适应连续药物筛选的实际应用。这些扩展提出了与序数响应建模相关的具有挑战性的研究问题。结果是多重的和延迟的。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.