Adaptive randomized designs for cancer clinical trials by using integer algorithms and exact Monte Carlo methods

使用整数算法和精确蒙特卡罗方法进行癌症临床试验的自适应随机设计

• 批准号: 10405326
• 负责人: Guogen Shan
• 金额: $ 7.63万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-14 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10405326
• 关键词: Affect; Algorithms; Analysis of Covariance; Budgets; Characteristics; Clinical Research; Clinical Trials; Clinical Trials Design; Clinical effectiveness; Computer software; Computers; Confidence Intervals; Data; Development; Drug Costs; Effectiveness; Enrollment; Goals; Inferior; Intuition; Link; Maintenance; Malignant Neoplasms; Methods; Monte Carlo Method; Names; Nature; Outcome; Participant; Patients; Phase; Phase II Clinical Trials; Population; Property; Randomized; Randomized Clinical Trials; Research Design; Sample Size; Sampling; Schools; Selection Bias; Time; Time Study; Treatment Failure; anticancer research; arm; base; cancer clinical trial; cost; design; drug development; flexibility; phase 2 designs; primary endpoint; programs; response; software development; success; supercomputer; treatment arm; two-arm study; web site

Project Summary/Abstract Current phase II clinical trial designs for cancer studies are generally not flexible and effective enough to reduce sample size and costs. Unlike traditional single-arm two-stage designs, adaptive designs allow a study to be modified with the information observed from previous stages. Recently, a few adaptive designs were developed for phase II cancer clinical trials with binary endpoints, and the majority of them cannot be directly applied in practice because of a counter-intuitive feature of the relationship between sample size and the number of responses from previous stages. We developed a new single-arm two-stage design that corrects that counter- intuitive feature of the study design. These adaptive designs were all developed for single-arm studies. In Aim 1, we will use efficient integer algorithms along with exact Monte Carlo simulation methods to develop adaptive randomized two-arm designs for cancer clinical trials. The proposed adaptive randomized designs are expected to save between 10% to 35% sample sizes as compared to the conventional group sequential designs. Unlike the existing adaptive randomized designs minimizing expected treatment failures, we will develop the first adaptive randomized designs with the objective to minimize expected sample size. For the existing adaptive single-arm design using integer algorithms without importance sampling, it could take a few months by using a stand-alone computer, and a few days using a supercomputer. With multiple arms in a study, it would be very computationally intensive. The goal of Aim 3 is to reduce the computation time to no more than 30 minutes by utilizing importance sampling and integer algorithms on a stand-alone computer. The traditionally used importance sampling does not guarantee the type I error rate and power. For this reason, we will utilize the recently developed exact importance sampling method to guarantee type I error rate and power. A combination of integer algorithms and importance sampling will be able to reduce the computation time to no more than 30 minutes for the proposed adaptive designs. In addition to new adaptive design development, we will also develop proper statistical inference for adaptive two-stage clinical trials in Aim 2. The existing exact approaches from commercial software for statistical inference are often based on the conditional framework, by assuming both marginal totals fixed. Such exact conditional approaches are not aligned with the study design for a clinical trial which often only assumes the sample size of each arm fixed, not the total responses. The proposed exact statistical inferences are proper by considering the nature of adaptive designs with multiple stages and sample size change. Ultimately, we will develop adaptive randomized designs for phase II cancer studies with binary endpoints with the smallest expected sample size. The proposed designs will be available for public use through a new R package and a new website that will use a powerful supercomputer. Upon completion of this project, our school will take over the cost of maintenance of the software developed from this proposal.

项目摘要/摘要 目前用于癌症研究的II期临床试验设计通常不具备足够的fl可行性和有效性，不足以减少 样本大小和成本。与传统的单臂两阶段设计不同，自适应设计允许研究 莫迪用从前几个阶段观察到的信息进行了fi。最近，一些自适应设计被开发出来 用于具有双终点的II期癌症临床试验，其中大多数不能直接应用 在实践中，因为样本大小和数量之间的关系具有与直觉相反的特征 前几个阶段的答复。我们开发了一种新的单臂两级设计，纠正了 直观的书房设计特点。这些适应性设计都是为单臂研究开发的。在AIM 1.我们将使用有效的fi整数算法和精确的蒙特卡罗模拟方法来开发自适应 癌症临床试验的随机双臂设计。所提出的自适应随机设计是可望的。 与传统的成组顺序设计相比，可节省10%至35%的样本量。不像 现有的自适应随机设计将预期治疗失败降至最低，我们将开发first 自适应随机设计，目标是最小化预期样本量。对于现有的自适应 单臂设计使用整数算法而不需要重要抽样，如果使用 一台独立的计算机，以及几天使用超级计算机的时间。在一个研究中有多个手臂，这将是非常 计算密集的。目标3的目标是将计算时间减少到不超过30分钟 在一台独立的计算机上利用重要性采样和整数算法。传统上使用的 重要性抽样不能保证I类误码率和功率。因此，我们将利用 最近提出了精确重要性抽样方法，以保证I类误码率和功率。一种组合 的整数算法和重要性采样将能够将计算时间减少到不超过 30分钟用于建议的自适应设计。除了新的适应性设计开发外，我们还将 为目标2中的适应性两阶段临床试验制定适当的统计推断。现有的确切方法 从商业软件进行统计推断往往都是基于条件框架，通过假设 两个边际总和fix。这种确切的有条件的方法与临床研究设计不一致。 该试验通常只假设每个ARMfix的样本大小，而不是总回答数。建议的确切 考虑到多阶段、多样本的适应性设计的性质，统计推断是适当的 大小变了。最终，我们将为II期癌症研究开发自适应随机设计， 具有最小预期样本量的端点。拟议的设计将通过以下途径供公众使用 一个新的R包和一个将使用强大的超级计算机的新网站。在这个项目完成后， 我们学校将承担根据该提案开发的软件的维护费用。