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

在药物在人群中普遍使用之前，通过临床试验对药物进行评估，以确定它们是安全有效的。规划这些临床试验以确保它们能够提供明确的答案通常需要数月时间，并根据要测试的药物、要纳入的患者数量、招募这些患者所需的试验持续时间以及为评估药物是否如希望的那样起作用而收集的数据来谨慎地做出决定。当一种新的疾病，如新冠肺炎，出现时，既有尽快开始临床试验的愿望，也有相当大的不确定性，不确定这些试验到底应该如何进行。可能被感染的人数，衡量这种新疾病治疗效果的最佳方式，甚至是测试最好的治疗方法，都可能是未知的。在这样的环境中，一种有用的方法是适应性设计。这使得临床试验一旦开始，就可以通过多种方式进行修改。一种适应性设计是平台试验设计，在试验开始后可以纳入更多的药物，如果数据表明它们不够有希望，可能同时将以前正在研究的药物从试验中删除，从而提供相当大的灵活性。尽管这样的设计并不新鲜，但在确保错误地表明新药有效的风险保持在可接受的低水平的最佳方法上，一些统计学问题仍然存在。此外，以前提出的方法通常不能提供新出现疾病临床试验所需的灵活性水平。如果试验的结论足够清楚，适应性试验就可以停止。然而，为了控制I型错误率，大多数试验设计要求预先指定试验中可以包括的最大患者数量。在大多数疾病环境中，这不构成挑战，因为有关于可能被招募的患者数量的良好信息。这与新出现疾病的背景形成对比，在新出现疾病的背景下，流行病的范围和持续时间可能存在相当大的不确定性。在这种情况下，在疫情持续期间继续招募尽可能多的患者可能是可取的。关于在平台试验中可能可用来测试的实验处理的数量可能存在类似的不确定性，其中使用现有方法的类型I差错率控制再次要求预先指定这一点。临床试验设计和分析的标准方法要求预先指定用于评估实验治疗的主要终点。在新出现的疾病中，可能存在关于最佳终点的不确定性，可能希望基于一个终点来计划和启动临床试验，但随着试验的进行，基于来自试验的数据以及来自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来解决关于患者数量、要评估的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这三个挑战。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在不断发展的流行病环境中适应新的知识来调整试验。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，我们将与主要利益攸关方组织研讨会，包括具有新发传染病专业知识的临床医生、具有这一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员，以及相关监管机构的代表。最后，我们将为患者和公众制作一个网络研讨会，解释临床试验的平台设计。

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.