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

项目总结/摘要 目前癌症研究的II期临床试验设计通常不够灵活和有效， 样本量和成本。与传统的单臂两阶段设计不同，自适应设计允许研究 用前几个阶段观察到的信息修改了艾德。最近，一些自适应设计被开发出来 对于具有二元终点的II期癌症临床试验，其中大多数不能直接应用 在实践中，由于样本量和数量之间的关系具有反直觉的特征， 前几个阶段的回应。我们开发了一种新的单臂两阶段设计，纠正了这种反- 研究设计的直观特征。这些自适应设计均为单组研究开发。在Aim中 1，我们将使用有效的整数算法沿着与精确的蒙特卡罗模拟方法，以开发自适应 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预期的 与传统的成组序贯设计相比，可节省10%至35%的样本量。不像 现有的适应性随机设计最大限度地减少预期的治疗失败，我们将开发第一个 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，没有重要性抽样，使用 一台独立的计算机，还有几天使用超级计算机。如果在一项研究中使用多个手臂， 计算密集型。目标3的目标是将计算时间减少到不超过30分钟 通过在独立计算机上利用重要性采样和整数算法。传统使用的 重要性抽样不能保证I类错误率和功效。因此，我们将利用 最近开发的精确重要性抽样方法，以保证第一类错误率和功率。的组合 的整数算法和重要性抽样将能够减少计算时间不超过 30分钟用于建议的自适应设计。除了新的适应性设计开发，我们还将 为目标2中的适应性两阶段临床试验开发适当的统计推断。现有的精确方法 从商业软件的统计推断往往是基于条件框架，通过假设 两个边际总数都固定了。这种精确的条件方法与临床研究的研究设计不一致。 试验，通常只假设每个组的样本量固定，而不是总响应。建议的精确 考虑到多阶段和多样本自适应设计的性质，统计推断是适当的 尺寸变化。最终，我们将为II期癌症研究开发适应性随机设计， 具有最小预期样本量的终点。建议的设计会透过 一个新的R软件包和一个新的网站，将使用一个强大的超级计算机。在这个项目完成后， 我校将承担根据这一建议开发的软件的维护费用。

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.