STatistical Methods for OPtimal Basket Trial Designs fOR Precision Medicine – a General, Customizable TOolbox (STOP OR GO)

精准医学最佳篮子试验设计的统计方法——通用、可定制的工具箱（停止或继续）

• 批准号: 459934212
• 负责人: Professor Dr. Meinhard Kieser, Ph.D.
• 金额: --
• 依托单位: Institut für Medizinische Biometrie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/459934212?language=en
• 关键词: STatistical; Methods; OPtimal; Basket; Trial

The demand for new treatments in precision medicine remains constantly high. New findings suggest incorporating knowledge on molecular profiling by targeting aberrant genes or pathways right from the beginning of drug development. Therefore, the development of new study designs for early clinical phases is of urgent need. A relatively new approach are so-called “Basket” designs. Basket trials are based on the idea of combining several substudies according to molecular profiling. The individual substudies thereby share a common design. The primary endpoint is usually binary. Other design options might vary, for example the number of study arms per substudy. Basket trials are designed to incorporate interim modification so they include adaptive design elements. By this, they have the chance to increase the efficiency of drug development and to allow developing individualized treatment approaches. However, Basket designs also come along with major statistical challenges including a more difficult sample size planning, multiple testing, interim analyses, the choice of optimal futility boundaries, definition of adequate go/no go criteria and rules for incorporating information exchange (“borrowing”) between different substudies.The global objective of this proposal is to develop optimal Basket designs, which incorporate isolated or combined adaptive elements such that • Clustering/ borrowing, • Futility stopping at interim, • Sample size recalculation. To do so, the proposal divides into three work packages:1. Within the first work package, we aim to improve and extend by reformulating published Basket designs as optimization problems with constraints, which shall be solved analytically. This approach allows 1) to find the optimal parameter solution for a given design and 2) to compare different Basket designs against each other.2. Within the second work package, we aim to develop new adaptive design elements, which are justified by thorough methodologic considerations such that specific performance requirements are met. 3. In a final work package, we will combine the developed constrained optimization problems with the newly adaptive design elements. Subsequently, we will implement a corresponding validated R package with graphical user interface on R CRAN, which allows to design an optimal customizable Basket trial by combining arbitrary adaptive design elements. The results of this project will help to establish efficient novel Basket trials designs in precision medicine. Application of the new designs in practice is supported by the developed validated, freely-available and easy to use software solution.

对精准医疗新疗法的需求仍然很高。新的研究结果表明，从药物开发的一开始就通过靶向异常基因或途径来整合分子特征分析的知识。因此，迫切需要为早期临床阶段开发新的研究设计。一种相对较新的方法是所谓的“篮式”设计。篮式试验基于根据分子谱分析将几个亚组研究相结合的想法。因此，各个子研究具有共同的设计。主要终点通常是二元的。其他设计选项可能会有所不同，例如每个子研究的研究组数量。网篮试模设计为包含临时修改，因此包含适应性设计元素。通过这种方式，他们有机会提高药物开发的效率，并允许开发个性化的治疗方法。然而，篮式设计也沿着主要的统计学挑战，包括更困难的样本量规划、多重检验、中期分析、最佳无效边界的选择、充分的通过/不通过标准的定义以及纳入信息交换的规则（“借用”）。本提案的总体目标是开发最佳篮子设计，其包含独立或组合的自适应元件，使得· 集群/借用，· 临时停止无效，· 样本量重新计算。为此，该提案分为三个工作包：1。 在第一个工作包中，我们的目标是通过将已发布的篮子设计重新表述为具有约束的优化问题来改进和扩展，这些问题应通过分析来解决。这种方法允许1）找到给定设计的最佳参数解决方案; 2）比较不同的篮子设计。 在第二个工作包，我们的目标是开发新的自适应设计元素，这是合理的彻底的方法论考虑，以满足特定的性能要求。3. 在最后的工作包，我们将联合收割机开发的约束优化问题与新的自适应设计元素。随后，我们将在R CRAN上实现一个相应的经过验证的具有图形用户界面的R包，它允许通过组合任意自适应设计元素来设计一个最佳的可定制篮子试验。该项目的结果将有助于在精准医学中建立有效的新型篮式试验设计。新设计在实践中的应用得到了已开发的经过验证、免费提供且易于使用的软件解决方案的支持。