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: 10329938
• 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 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10329938
• 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.

项目概要/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，无法减少 与传统的单臂两阶段设计不同，自适应设计允许研究 最近，根据之前阶段观察到的信息进行了修改，开发了一些自适应设计。 对于具有二元终点的癌症II期临床试验，大多数不能直接应用 在实践中，由于样本量和样本数量之间的关系存在违反直觉的特征 我们开发了一种新的单臂两级设计来纠正这种反作用。 研究设计的直观特征。这些自适应设计都是为单臂研究而开发的。 1、我们将使用高效的整数算法以及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组设计是预期的。 与传统的组序贯设计相比，可节省 10% 至 35% 的样本量。 现有的自适应随机设计最大限度地减少了预期的治疗失败，我们将开发第一个 自适应随机设计，旨在最大限度地减少预期样本量。 使用整数算法的单臂设计没有重要性采样，使用它可能需要几个月的时间 一台独立计算机，几天使用一台超级计算机进行研究，这将是非常了不起的。 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传统上使用的重要性采样和整数算法。 重要性采样不能保证 I 类错误率和功效，因此，我们将利用 最近开发了精确重要性采样方法来保证I类错误率和功效的组合。 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 除了新的自适应设计开发，我们还将进行。 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。现有的确切方法 来自商业软件的统计推断通常基于条件框架，通过假设 两个边际总数都是固定的。 试验通常只假设每个臂的样本量是固定的，而不是所建议的确切响应。 通过考虑多阶段和样本的自适应设计的性质，统计推断是正确的 最终，我们将为二元癌症研究开发适应性随机设计。 所提议的设计将通过以下方式供公众使用。 一个新的 R 包和一个将使用强大的超级计算机的新网站。 我校将承担根据本提案开发的软件的维护费用。

项目成果

Continuity corrected Wilson interval for the difference of two independent proportions.

连续性修正了两个独立比例差异的威尔逊区间。

• DOI: 10.1007/s44199-023-00054-8
• 发表时间: 2023
• 期刊: Journal of statistical theory and applications : JSTA
• 作者: Shan,Guogen;Lou,XiangYang;Wu,SamuelS
• 通讯作者: Wu,SamuelS

New Confidence Intervals for Relative Risk of Two Correlated Proportions.

• DOI: 10.1007/s12561-022-09345-7
• 发表时间: 2023
• 期刊: Statistics in biosciences
• 影响因子: 1
• 作者: DelRocco N;Wang Y;Wu D;Yang Y;Shan G
• 通讯作者: Shan G

Comparison of Pocock and Simon's covariate-adaptive randomization procedures in clinical trials.

• DOI: 10.1186/s12874-024-02151-3
• 发表时间: 2024-01-25
• 期刊: BMC medical research methodology
• 影响因子: 4

Optimal two-stage designs based on restricted mean survival time for a single-arm study.

• DOI: 10.1016/j.conctc.2021.100732
• 发表时间: 2021-03
• 期刊: Contemporary clinical trials communications
• 影响因子: 1.5
• 作者: Shan G

ADSS: A Composite Score to Detect Disease Progression in Alzheimer's Disease.

ADSS：检测阿尔茨海默病疾病进展的综合评分。

• DOI: 10.3233/adr-230043
• 发表时间: 2024
• 期刊: Journal of Alzheimer's disease reports
• 作者: Shan,Guogen;Lu,Xinlin;Li,Zhigang;Caldwell,JessicaZK;Bernick,Charles;Cummings,Jeffrey
• 通讯作者: Cummings,Jeffrey