New adaptive platform designs for clinical trials in an emerging disease epidemic

用于新兴疾病流行病临床试验的新适应性平台设计

• 批准号: MR/V038419/1
• 负责人: Nigel Stallard
• 金额: $ 53.07万
• 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: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV038419%2F1
• 关键词: New; adaptive; platform; designs; clinical

Before they are made available for general use in the population, drugs are evaluated in clinical trials to determine that they are safe and effective. The planning of these clinical trials to ensure they are able to provide definitive answers usually takes many months, with decisions being carefully depending on the drugs to be tested, the number of patients to be included, the trial duration required to recruit this number of patients and the data collected to assess whether or not the drugs work as hoped. When a new disease, such as COVID-19, emerges, there is a both a desire to start clinical trials as soon as possible and considerable uncertainty over exactly how these trials should proceed. The number of people likely to be infected, the best way to measure treatment effectiveness in the new disease, and even the best treatments to test, may all be unknown. A useful approach in such a setting is an adaptive design. This allows a clinical trial, once started, to be modified in a number of ways. One type of adaptive design is a platform trial design, in which additional drugs can be included after the trial has commenced, possibly at the same time as drugs previously under investigation are dropped from the trial if the data suggest that they are not sufficiently promising, giving considerable flexibility. Although such designs are not new, a number of statistical questions remain over the best approach to ensure that the risk of erroneously indicating that a new drug is effective, is kept acceptably low. Additionally, previously proposed methods do not usually provide the level of flexibility desired for clinical trials in an emerging disease. An adaptive trial can be stopped if the conclusions of the trial are sufficiently clear. In order to control the type I error rate, however, most trial designs require the maximum number of patients that can be included in the trial to be specified in advance. In most disease settings, this presents no challenge as there is good information on the number of patients likely to be recruited. This is in contrast to the setting of an emerging disease, when there can be considerable uncertainty regarding the extent and duration of an epidemic. In this case it might be desirable to continue to recruit as many patients as possible while an outbreaks persists. Similar uncertainty can exist regarding the number of experimental treatments that might be available to be tested in a platform trial, with type I error rate control using existing methods again requiring this to be specified in advance. Standard methods for clinical trial design and analysis require specification in advance of a primary endpoint used for the evaluation of experimental treatments. In an emerging disease, there may be uncertainty regarding the best endpoint, and it might be desirable to plan and start a clinical trial based on one endpoint, but to modify this as the trial progresses based on data from the trial as well as information from external sources.This project will develop novel statistical methods to solve the three challenges of uncertainty over the number of patients, the number and timing of treatments to be evaluated, and the best endpoint to be used in the evaluation. This will provide valid analysis methods for clinical trials that have the flexibility needed to enable clinical investigators to adapt the trials in reaction to new knowledge in a developing epidemic setting. To ensure that the methods we develop are widely disseminated and have maximum impact on clinical trial practice, we will organise a workshop with key stakeholders including clinicians with expertise in emerging infectious diseases, clinical trialists with experience in this area, statisticians with expertise in adaptive trial designs, and relevant regulatory body representatives. Finally, we will produce a webinar to for patients and the general public to explain platform designs for clinical trials.

在药物在人群中普遍使用之前，需要通过临床试验对其进行评估，以确定它们的安全性和有效性。为确保能够提供明确答案而进行的临床试验规划通常需要数月的时间，并根据要测试的药物、要纳入的患者数量、招募这些患者数量所需的试验持续时间以及评估药物是否按预期发挥作用而收集的数据仔细做出决定。当出现一种新疾病（例如 COVID-19）时，人们既希望尽快开始临床试验，又对这些试验应如何进行存在很大的不确定性。可能被感染的人数、衡量新疾病治疗效果的最佳方法，甚至是要测试的最佳治疗方法，都可能是未知的。在这种情况下，一种有用的方法是自适应设计。这使得临床试验一旦开始就可以通过多种方式进行修改。适应性设计的一种类型是平台试验设计，其中可以在试验开始后纳入额外的药物，如果数据表明它们没有足够的前景，可能同时将之前正在研究的药物从试验中删除，从而提供了相当大的灵活性。尽管这样的设计并不新鲜，但对于确保错误表明新药有效的风险保持在可接受的低水平的最佳方法，仍然存在许多统计问题。此外，先前提出的方法通常不能提供新出现疾病的临床试验所需的灵活性水平。如果试验的结论足够明确，则可以停止适应性试验。然而，为了控制I型错误率，大多数试验设计都要求预先指定试验中可纳入的最大患者数量。在大多数疾病环境中，这不构成任何挑战，因为有关于可能招募的患者数量的良好信息。这与新出现疾病的情况形成鲜明对比，新出现疾病的流行程度和持续时间可能存在相当大的不确定性。在这种情况下，在疫情持续存在的情况下，可能需要继续招募尽可能多的患者。关于可在平台试验中测试的实验处理的数量也可能存在类似的不确定性，使用现有方法的 I 类错误率控制再次要求提前指定。临床试验设计和分析的标准方法需要提前指定用于评估实验治疗的主要终点。在一种新出现的疾病中，最佳终点可能存在不确定性，可能需要根据一个终点来计划和启动临床试验，但随着试验的进展，根据试验数据以及外部来源的信息来修改这一点。该项目将开发新的统计方法，以解决患者数量、待评估治疗的数量和时间以及评估中使用的最佳终点的不确定性的三个挑战。这将为临床试验提供有效的分析方法，这些方法具有所需的灵活性，使临床研究人员能够根据流行病发展中的新知识来调整试验。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，我们将组织一次由主要利益相关者参加的研讨会，包括具有新发传染病专业知识的临床医生、具有该领域经验的临床试验人员、具有适应性试验设计专业知识的统计学家以及相关监管机构代表。最后，我们将为患者和公众举办一次网络研讨会，解释临床试验的平台设计。