Statistical Methods and Issues for Implementing Adaptive Phase I Trials

项目总结/摘要 本建议中的六个目标是由适应性第一阶段设计中的重要和相关问题激发的 需要重点研究的试验。这项提案中的调查人员带来了一个独特的强大组合， 统计方法，应用临床试验经验和计算机编程技能，以影响未来的 肿瘤临床试验成功完成拟议的研究将大大增加现有的阶段 我的方法，并提供新的适应性I期试验方法，这将是重要的， 方法论和应用统计学家。最重要的是，我们的发现将与NIH的使命有关， 改善人类健康和拯救生命的重大发现。目标1涉及模型中的基本问题 持续再评估方法（CRM）的构建，在已发表的文献中被忽略，但有一个 直接影响审判的成功。目标2、3和4的共同主题是提高效率， 通过将额外的患者信息纳入剂量探索过程，这些信息 与患者的既往病史以及试验期间的癌症治疗有关， 常规收集用于一般临床目的，可提供有关 一种药物，但在I期研究中经常被忽视。目标2提出了四种建模方法， 适应性设计中的患者异质性，目的3研究了两种纳入非剂量限制性 将毒性纳入MTD估计中，而目标4则检查纳入非单调疗效的方法 模式.目的5提出了在I期试验完成后推断每种剂量DLT发生率的方法。 完成入组，试验完成后很少考虑的信息和推荐的MTD 但提供了所选MTD及其邻近剂量周围的不确定性信息。 缺乏免费提供和可修改的软件仍然是实施自适应 I期试验设计纳入常规临床实践。因此，本提案包含最后一个目标，即第六个目标， 整个四年的提案，只集中在编程，在SAS和R，所有的方法描述 在这个提议中，以及现有的方法，尚未被容纳在一个单一的软件包。通过 这最终的目标，我们将提供一套软件包，以满足研究人员的一般需要， I期设计方法和I期临床试验管理人员的具体日常需求， 最终目标是使适应性I期试验设计在发表在《肿瘤学杂志》上的肿瘤学试验中变得司空见惯。 未来十年。

PROJECT SUMMARY/ABSTRACT The six aims in this proposal are motivated by important and relevant issues in the design of adaptive Phase I trials where focused research is needed. The investigators in this proposal bring a uniquely strong combination of statistical methodology, applied clinical trial experience, and computer programming skills to impact the future of oncology clinical trials. Successful completion of the proposed research will substantially augment existing Phase I methodology and provide new insight into novel adaptive Phase I trials approaches that will be important to both methodologic and applied statisticians. Most important, our findings will be relevant to the NIH mission of making important discoveries that improve peoples health and save lives. Aim 1 concerns fundamental issues in model construction for the Continual Reassessment Method (CRM) that are ignored in published literature but have a direct impact on the success of the trial. Aims 2, 3, and 4 share an underlying theme of improving the efficiency of Phase I trials by incorporating additional patient information into the dose-finding process. This information relates to both the prior history of the patient as well as the treatment of their cancer during the trial, data that are routinely collected for general clinical purposes that could impart additional information about the toxicity profile of an agent, but that are often ignored in Phase I studies. Aim 2 proposes four modeling approaches to incorporate patient heterogeneity in adaptive designs, Aim 3 investigates two approaches for incorporating non-dose-limiting toxicities into the estimation of the MTD, while Aim 4 examines approaches to incorporate non-monotonic efficacy patterns. Aim 5 proposes methods for inference about the DLT rate for each dose after a Phase I trial has completed enrollment, information that is rarely considered once a trial is completed and the recommended MTD is found, but provides information for the uncertainty surrounding the selected MTD and its neighboring doses. The lack of freely available and modifiable software remains the major barrier to the implementation of adaptive Phase I trial designs into routine clinical practice. Therefore, this proposal contains a final, sixth aim, spanning all four years of the proposal, focused solely on the programming, in both SAS and R, of all methods described in this proposal, as well as existing methods that have yet to be housed in a single software package. Through this final aim, we will provide a suite of software packages that meet the general needs of researchers working on Phase I design methodology and the specific day-to-day needs of those who administer actual Phase I trials, with the eventual goal of making adaptive Phase I trial designs commonplace in oncology trials published in the coming decade.