Statistical Methods and Issues for Implementing Adaptive Phase I Trials

实施适应性 I 期试验的统计方法和问题

• 批准号: 8205029
• 负责人: Thomas M Braun
• 金额: $ 27.29万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-08 至 2013-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8205029
• 关键词: Accounting; Address; Clinical; Clinical Research; Clinical Trials; Clinical Trials Design; Computer software; Data; Dose; Dose-Limiting; Drug Formulations; Enrollment; Event; Future; Goals; Grant; Health; Heterogeneity; Housing; Journals; Literature; Logistic Regressions; Malignant Neoplasms; Methodology; Methods; Mission; Modeling; New Agents; Patients; Pattern; Phase; Phase I Clinical Trials; Process; Publishing; Recording of previous events; Research; Research Personnel; Statistical Methods; Tail; Time; Toxic effect; Uncertainty; United States National Institutes of Health; Work; cancer therapy; clinical practice; computer program; design; drug discovery; experience; improved; insight; meetings; novel; oncology; phase 1 study; programs; skills; software development; success; user friendly software

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.

项目摘要/摘要 本提案中的六个目标是由自适应第一阶段设计中的重要和相关问题推动的 需要重点研究的试验。这个提案中的调查人员带来了一个独特的强大的组合 统计方法、应用临床试验经验和计算机编程技能，以影响 肿瘤学临床试验。成功完成拟议的研究将大大加强现有阶段 I方法论，并提供了对新的适应性I期试验方法的新见解，这些方法将对两者都很重要 方法论和应用统计学家。最重要的是，我们的发现将与NIH的任务相关 改善人民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 已发表文献中忽略的连续再评估方法(CRM)的构建 直接影响到审判的成败。目标2、目标3和目标4都有一个共同的主题：提高效率 通过将更多的患者信息纳入剂量发现过程，对I期试验进行评估。此信息 与患者的既往病史以及在试验期间对其癌症的治疗有关，这些数据是 常规收集用于一般临床用途，可提供有关其毒性分布的额外信息 一种制剂，但在I期研究中往往被忽视。Aim 2提出了四种建模方法来结合 适应性设计中的患者异质性，Aim 3研究了两种纳入非剂量限制的方法 毒性纳入MTD的估计，而目标4检查了纳入非单调疗效的方法 模式。目的5提出推断I期试验后每个剂量的DLT率的方法 已完成注册、试用完成后很少考虑的信息以及建议的MTD 但提供了围绕所选MTD及其邻近剂量的不确定性的信息。 缺乏可自由使用和可修改的软件仍然是实施自适应的主要障碍 将I期试验设计纳入常规临床实践。因此，这项提案包含了最后的，也就是第六个目标 提案的所有四年，仅侧重于以SAS和R编制所述所有方法的方案 在这项提议中，以及尚未纳入单一软件包的现有方法。穿过 这一最终目标，我们将提供一套软件包，以满足研究人员工作的一般需求 关于第一阶段设计方法和实际第一阶段试验管理人员的具体日常需求， 最终目标是使适应性I期试验设计在肿瘤学试验中司空见惯，发表在 未来十年。