Statistical methods for interrupted clinical trials

大规模随机对照临床试验是包括新药、手术技术和行为干预在内的医疗保健治疗评估的重要组成部分。与生活中的许多其他部分一样，正在进行的临床试验也受到了全球冠状病毒大流行的影响。由于取消了非必要的医疗程序，对面对面评估的限制，以及由于封锁限制、疾病或不愿访问医院或医疗中心而导致的门诊缺勤，导致许多正在进行的临床试验的招募和数据收集被暂停。随着限制开始放松，研究人员有机会重新启动被中断的临床试验。然而，这是否值得这样做，或者在重启或未重启的试验中分析数据的最佳方式，可能会带来一些挑战。该项目将研究统计工具，以帮助解决这些问题。这些方法在其他情况下也将是有价值的，当试验因招募或资助方面的挑战或由于其他研究的新结果的影响而中断时。如果重新启动试验，取决于进行试验的临床领域，大流行前和大流行后时期可能会在登记参加试验的患者类型、测量的确切方式，甚至在评估干预措施方面存在差异，例如，对于可能已将交付改为完全或部分在线的心理干预。这些差异或异质性需要在统计分析中考虑在内，这可能意味着为了获得可靠的结果，需要纳入比最初预期更多的患者。我们将确定这种分析的方法，并在中断试验的情况下对这些方法进行评估。由于建议的分析方法被所有利益攸关方接受是重要的，我们将为临床试验人员、临床试验统计学家以及监管机构、资助者、科学出版商和患者的代表举办研讨会，讨论并有望就最合适的方法达成共识。如果不重新启动试验，纳入的患者数量将少于最初计划的数量。在许多情况下，特别是在最终评估治疗效果之前，在临床试验中对患者进行长期跟踪的情况下，可能会获得在大流行开始前不久招募的患者的一些早期数据。这些数据可能会提供更多的信息，这些信息可以纳入最终的分析。我们将探索统计方法，以便最好地利用这些数据中的可用信息，在必要的地方扩展现有的方法。除了开发和推荐分析重启或未重启试验的方法外，我们还将开发方法，根据已有的信息量以及大流行前和大流行后预期的不同程度，帮助决定哪种方法是最佳选择。我们还将制定方法，允许对已经收集的数据进行分析，但也允许在试验结果不够清楚的情况下重新开始试验。为了确保错误的假阳性试验结果的风险不会增加，这种分析需要专门的统计方法。研究团队包括临床试验统计专家以及来自一系列临床领域的试验者和试验资助者代表，以确保研究适用于广泛的临床试验环境。

Large-scale randomised controlled clinical trials are an essential part of the evaluation of healthcare treatments including new drugs, surgical techniques and behavioural interventions. Like many other parts of life, ongoing clinical trials have been affected by the global coronavirus pandemic. The cancellation of non-essential medical procedures, restrictions on face-to-face assessments and outpatient non-attendance due to lockdown restrictions, illness or reluctance to visit hospitals or healthcare centres have led to recruitment and data collection being suspended for many ongoing clinical trials.As restrictions start to be relaxed, researchers have the opportunity to restart clinical trials that were interrupted. The questions of whether or not this is worth doing, or of the best way to analyse the data either in a restarted trial or in one that is not restarted, may raise some challenges, however. This project will research statistical tools to help address these questions. These methods will also be of value in other settings when trials are interrupted due to challenges in recruitment or funding, or due to the influence of new results from other research.If a trial is restarted, depending on the clinical area in which the trial is being conducted, there may be differences between the pre-pandemic and post-pandemic periods in the type of patients who enrol in the trial, the exact way in which measurements are taken, or even in the intervention to be assessed, for example for a psychological intervention for which delivery may have changed to being wholly or partially online. These differences, or heterogeneity, need to be accounted for in the statistical analysis, and may mean that a larger number of patients than initially anticipated need to be included in the trial in order to obtain a reliable result. We will identify methods for this analysis and evaluate these in the setting of interrupted trials. As it is important that analysis methods proposed are accepted by all stakeholders, we will organise workshops for clinical trialists, clinical trial statisticians and representatives of regulators, funders, science publishers and patients to discuss and hopefully lead to consensus on the most appropriate methodology. If a trial is not restarted, the number of patients included will be smaller than initially planned. In many cases, particularly those in which patients are followed up in the clinical trial for a long period before the effect of the treatment is finally assessed, some early data may be available for patients recruited shortly before the start of the pandemic. This data may give additional information that can be included in the final analysis. We will explore statistical approaches to best utilise the information available in these data, extending existing methods where this is necessary. In addition to developing and recommending methods for the analysis of trials that are or are not restarted, we will develop methods to help decide which of these is the best option depending on the amount of information already available and the degree of heterogeneity between pre-pandemic and post-pandemic periods that is anticipated. We will also develop methods that allow an analysis of the data already collected but also allow the option of restarting the trial if the results of the trial are not sufficiently clear. Specialist statistical methods are required for this analysis in order to ensure that the risk of an erroneous false positive trial result is not increased.The research team includes experts in clinical trial statistics along with trialists and representatives of trial funders from a range of clinical areas to ensure that the research is applicable in a wide range of clinical trial settings.