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

在治疗可用于一般人群之前，通常在临床试验中对其进行评估，以确定其安全有效。这类试验的统计设计的一个主要驱动因素是确保它们能够为决策提供明确的答案。临床试验通常是昂贵的，在一种新的成功疗法能够达到大多数患者之前，整个开发过程可能需要几年时间。在许多情况下，例如危及生命的罕见疾病，强烈希望尽快将患者分配到潜在的上级干预（即，在审判过程中）。在这种情况下，将这一额外目标纳入临床试验的一种有用方法是使用反应自适应设计。这些设计使患者的分配倾向于新的干预措施，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将更多患者分配到一种干预措施中，该研究也可能导致收集的所有其他干预措施的证据水平较低，这反过来可能会阻碍对疗效问题提供明确的答案。反应适应性设计并不新鲜，已经提出了旨在在试验中提供患者获益的同时保留最终证据的完整性。然而，关键的统计和实践问题仍然存在于最佳方法上，以确保使用反应适应性设计的试验在干预有效的情况下具有很高的明确回答概率（而不需要不切实际的大样本量）。此外，任何增加在响应自适应设计后找到确定答案的机会的新方法，在没有干预措施有效时，仍然需要确保统计完整性。如果试验持续时间较长，并且患者特征的重要变量随时间发生变化（如平台试验中的情况），则后一项挑战更大。本项目将开发新的统计方法，以便在使用反应适应性设计时最大限度地提高识别有效干预的概率，从而为试验中的患者提供更高的机会接受上级干预。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预期比传统设计（每次干预固定分配）更好的结局。我们还将确保这些方法即使在不断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。这些建议和指南将从与关键利益相关者（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）举行的研讨会中获取信息