Developing and enabling efficient hypothesis test for response-adaptive design with patient benefit goals

开发并启用有效的假设检验，以实现具有患者利益目标的响应自适应设计

• 批准号: MR/Z503538/1
• 负责人: Sofia Villar
• 金额: $ 65.36万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FZ503538%2F1
• 关键词: Developing; enabling; efficient; hypothesis; test

Before therapies are made available for general use in the population, they are typically evaluated in clinical trials to determine that they are safe and effective. A main driver in the statistical design of such trials is to ensure they can provide definitive answers for decision-making. Clinical trials are usually expensive, and the full developmental process can take several years before a new successful therapy is able to reach most patients. In many settings, such as life-threatening rare diseases, there is a strong desire to allocate patients to a potentially superior intervention as soon as possible (i.e., during the trial itself). A useful approach to incorporate this additional goal into a clinical trial in such settings is to use a response-adaptive design. These designs skew the allocation of patients in favour of new interventions as long as they are showing promise during the trial. However, by possibly assigning more patients to an intervention during the trial, the study could also result in a lower level of evidence collected on all other interventions, which in turn could hinder the delivering definitive answers to the efficacy question.Response-adaptive designs are not new and have been proposed with the aim to deliver patient benefit within a trial while preserving integrity of the final evidence. However, key statistical and practical questions remain over the best approach to ensure that a trial using a response-adaptive design has a high probability of definitively answering if an intervention is effective (without requiring unrealistically large sample sizes to do so). Additionally, any new method that increases the chances of finding a definitive answer after a response-adaptive design would still need to ensure statistical integrity when no intervention is effective. The latter challenge is even greater if trials last for a long time period and important variables in the patients' characteristics change over time (as is the case in platform trials).This project will develop novel statistical methods to maximise the probability to identify efficacious interventions when using a response-adaptive design that offers patients in the trial a higher chance of receiving the superior intervention. This will provide valid analysis methods for clinical trials that offer the flexibility needed to enable patients within trials to expect a better outcome than in a traditional design with fixed allocations per intervention. We will also ensure these methods preserve validity even under changing temporal conditions. To ensure that the methods we develop are widely disseminated and have maximum impact on clinical trial practice, we will provide open-source software and recommendations for the use in practice of the produced methods. The recommendations and guidance will take input from a workshop with key stakeholders including statisticians with expertise in adaptive trial designs, clinicians, clinical trialists, relevant regulatory bodies and patient representatives

在将疗法广泛应用于人群之前，通常会在临床试验中对它们进行评估，以确定它们的安全性和有效性。此类试验统计设计的主要驱动力是确保它们能为决策提供明确的答案。临床试验通常很昂贵，并且完整的开发过程可能需要数年时间才能使新的成功疗法惠及大多数患者。在许多情况下，例如危及生命的罕见疾病，人们强烈希望尽快（即在试验期间）将患者分配到可能更好的干预措施。在这种情况下，将这一额外目标纳入临床试验的一个有用方法是使用响应自适应设计。这些设计使患者的分配偏向于新的干预措施，只要它们在试验期间显示出希望。然而，由于在试验期间可能分配更多患者接受干预，该研究也可能导致所有其他干预措施收集的证据水平较低，这反过来又可能阻碍对疗效问题提供明确的答案。这并不是什么新鲜事，提出的目的是为了在试验中为患者带来利益，同时保持最终证据的完整性。然而，关键的统计和实际问题仍然存在于最佳方法上，以确保使用响应自适应设计的试验有很高的概率明确回答干预措施是否有效（不需要不切实际的大样本量来做到这一点）。此外，任何在响应自适应设计后增加找到明确答案的机会的新方法仍然需要在没有有效干预的情况下确保统计完整性。如果试验持续很长时间并且患者特征的重要变量随着时间的推移而变化（就像平台试验中的情况），后一个挑战甚至更大。该项目将开发新的统计方法，以最大限度地提高识别有效干预措施的可能性当使用响应适应性设计时，可以为试验中的患者提供更高的接受优质干预的机会。这将为临床试验提供有效的分析方法，提供所需的灵活性，使试验中的患者能够比每次干预固定分配的传统设计获得更好的结果。我们还将确保这些方法即使在变化的时间条件下也保持有效性。为了确保我们开发的方法得到广泛传播并对临床试验实践产生最大影响，我们将提供开源软件和建议，以便在实践中使用所产生的方法。这些建议和指南将听取关键利益相关者参加的研讨会的意见，其中包括具有适应性试验设计专业知识的统计学家、临床医生、临床试验人员、相关监管机构和患者代表