Using Surrogate Endpoints for Decision-Making in Adaptive Seamless Designs

在自适应无缝设计中使用代理端点进行决策

• 批准号: G1001344/1
• 负责人: Nigel Stallard
• 金额: $ 34.75万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=G1001344%2F1
• 关键词: Using; Surrogate; Endpoints; Decision; Making

Clinical trials are used to evaluate the safety and effectiveness of new drugs and other novel therapies prior to general use. Conventionally, a major part of this evaluation is a trial to compare one new drug (at a single prespecified dose/formulation) with an existing standard or control treatment. Statistical methods have been developed over many years to allow valid interpretation of the data from such trials.A recent innovation in clinical trial design has been the development of new approaches in which several new treatments are compared simultaneously with the control in a single clinical trial, with the less effective treatments dropped from the trial on the basis of early results. Such methods, called adaptive seamless designs, have the potential to reduce the number of patients required. A particular challenge arises in chronic diseases when the data indicating treatment effectiveness may be observed only after long-term follow-up. For example, in Multiple Sclerosis the primary outcome considered might be a change in health status after three years of treatment. Often some other earlier outcome can be observed that it is believed is predictive of the primary long-term outcome. Such an outcome is called a surrogate endpoint. In the Multiple Sclerosis case, for example, imaging of the brain can give an early indication of disease progression. In this case it would seem sensible to use this surrogate endpoint information in addition to any available primary endpoint data to decide which treatments should be dropped from the trial. The development of statical methods that allow the use of surrogate endpoints for treatment selection in adaptive seamless designs has been an area of active research in the last few years. The difficulty comes when trying to ensure that the final comparison of the selected treatment (or treatments) is a fair one, since they could have been selected because they appeared superior to other treatments by chance.The aim of this project is to develop a new statistical method for using a surrogate endpoint in an adaptive seamless design. By making the most efficient use of the surrogate endpoint information, this new approach will lead to further reduction in the number of patients required in the trial. This will reduce the number of patients exposed to less effective treatments and ensure that effective new drugs are made available for general use, on the basis of thorough evaluation, as quickly as possible.

临床试验用于在普遍使用之前评估新药和其他新疗法的安全性和有效性。传统上，该评估的主要部分是对一种新药（以单一预先指定的剂量/配方）与现有标准或对照治疗进行比较的试验。统计方法已经发展了很多年，可以有效解释此类试验的数据。临床试验设计的最新创新是开发新方法，在单个临床试验中同时将几种新疗法与对照进行比较，并根据早期结果从试验中剔除效果较差的疗法。这种方法被称为自适应无缝设计，有可能减少所需的患者数量。当只有在长期随访后才能观察到表明治疗效果的数据时，慢性疾病会出现一个特殊的挑战。例如，在多发性硬化症中，考虑的主要结果可能是三年治疗后健康状况的变化。通常可以观察到一些其他早期结果，据信这些结果可以预测主要的长期结果。这样的结果称为替代终点。例如，在多发性硬化症病例中，大脑成像可以提供疾病进展的早期指示。在这种情况下，除了任何可用的主要终点数据之外，使用此替代终点信息来决定应从试验中删除哪些治疗似乎是明智的。允许在自适应无缝设计中使用替代终点进行治疗选择的静态方法的开发一直是过去几年的活跃研究领域。当试图确保所选治疗（或多个治疗）的最终比较是公平的时，困难就来了，因为它们可能被选择是因为它们偶然优于其他治疗。该项目的目的是开发一种新的统计方法，在自适应无缝设计中使用替代终点。通过最有效地利用替代终点信息，这种新方法将进一步减少试验所需的患者数量。这将减少接受效果较差治疗的患者数量，并确保在彻底评估的基础上尽快提供有效的新药供普遍使用。