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: 8022917
• 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/8022917
• 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; public health relevance; skills; software development; success; user friendly software

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: Phase I trials are a crucial first step in the discovery of new agents, either alone or in combination with existing agents, for the treatment of cancer. Successful drug discovery hinges on superior clinical trial designs, as well as freely available software to implement those designs. This proposal will examine needed improvements to adaptive Phase I trial designs and provide user-friendly software that facilitates the implementation of those improvements into actual clinical research. The findings will be relevant to the NIH mission and provide new insight to those who either design adaptive Phase I trials or administer actual adaptive Phase I trials.

描述（由申请人提供）：本提案中的六个目标是由适应性I期试验设计中的重要和相关问题驱动的，需要进行重点研究。本提案中的研究人员将统计学方法，应用临床试验经验和计算机编程技能相结合，以影响肿瘤临床试验的未来。拟议研究的成功完成将大大增强现有的第一阶段方法，并为新的适应性第一阶段试验方法提供新的见解，这对方法和应用统计学家都很重要。最重要的是，我们的发现将与NIH的使命相关，即做出改善人们健康和拯救生命的重要发现。目的1关注持续再评估方法（CRM）模型构建中的基本问题，这些问题在已发表的文献中被忽视，但对试验的成功有直接影响。目标2、3和4都有一个共同的基本主题，即通过将额外的患者信息纳入剂量确定过程来提高I期试验的效率。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，这些数据是常规收集的，用于一般临床目的，可以提供有关药物毒性特征的额外信息，但在I期研究中经常被忽略。目标2提出了四种建模方法，将患者异质性纳入适应性设计，目标3研究了两种将非剂量限制性毒性纳入MTD估计的方法，而目标4研究了纳入非单调疗效模式的方法。目标5提出了在I期试验完成入组后推断每个剂量的DLT率的方法，这些信息在试验完成并找到推荐的MTD后很少被考虑，但提供了围绕所选MTD及其邻近剂量的不确定性的信息。缺乏可自由获取和可修改的软件仍然是将适应性I期试验设计应用于常规临床实践的主要障碍。因此，本提案包含了最后的第六个目标，跨越了提案的所有四年，只关注在SAS和R中编程，本提案中描述的所有方法，以及尚未包含在单个软件包中的现有方法。通过这一最终目标，我们将提供一套软件包，以满足研究I期设计方法的研究人员的一般需求，以及管理实际I期试验的人员的具体日常需求，最终目标是在未来十年内在肿瘤试验中发表适应性I期试验设计。