Statistical methods for interrupted clinical trials

中断临床试验的统计方法

• 批准号: MR/W021013/1
• 负责人: Nigel Stallard
• 金额: $ 51.92万
• 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%2FW021013%2F1
• 关键词: Statistical; methods; interrupted; clinical; trials

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.

大规模随机对照临床试验是评估医疗保健治疗的重要组成部分，包括新药，手术技术和行为干预。与生活中的许多其他方面一样，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。非必要医疗程序的取消、面对面评估的限制以及因封锁限制、疾病或不愿前往医院或医疗中心而导致的门诊缺席，导致许多正在进行的临床试验的招募和数据收集暂停。随着限制开始放松，研究人员有机会重新启动中断的临床试验。然而，是否值得这样做，或者在重新开始的试验或没有重新开始的试验中分析数据的最佳方法，这些问题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。当试验因招募或资金困难或受其他研究新结果的影响而中断时，这些方法也将具有价值。如果试验重新开始，根据试验正在进行的临床领域，大流行前和大流行后期间入组试验的患者类型可能存在差异，采取测量的确切方式，或者甚至在要评估的干预中，例如对于心理干预，其交付可能已经改变为全部或部分在线。这些差异或异质性需要在统计分析中考虑，并且可能意味着为了获得可靠的结果，需要将比最初预期更多的患者纳入试验。我们将确定这种分析的方法，并在中断试验的情况下评估这些方法。由于所有利益相关者都接受拟议的分析方法非常重要，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组织研讨会，以讨论并希望就最合适的方法达成共识。如果试验没有重新开始，则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随访的情况下，可能会获得大流行开始前不久招募的患者的一些早期数据。这些数据可以提供额外的信息，可以包括在最终分析中。我们将探索统计方法，以最好地利用这些数据中的信息，并在必要时扩展现有方法。除了开发和推荐用于分析重新启动或未重新启动的试验的方法外，我们还将开发方法来帮助决定哪一种是最佳选择，这取决于现有信息的数量以及预期的大流行前和大流行后时期之间的异质性程度。我们还将开发允许分析已收集数据的方法，但如果试验结果不够明确，也允许选择重新开始试验。该分析需要专业的统计方法，以确保不增加错误的假阳性试验结果的风险。研究团队包括临床试验统计学专家沿着来自多个临床领域的试验人员和试验资助者代表，以确保研究适用于广泛的临床试验环境。

项目成果

Bayesian and Frequentist Approaches to Rescuing Disrupted Trials: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

• DOI: 10.1080/19466315.2024.2313986
• 发表时间: 2024-03-14
• 期刊: STATISTICS IN BIOPHARMACEUTICAL RESEARCH
• 影响因子: 1.8
• 作者: Kunz，Cornelia Ursula;Tarima，Sergey;Flournoy，Nancy
• 通讯作者: Flournoy，Nancy

Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

• DOI: 10.1080/19466315.2023.2211023
• 发表时间: 2022-06
• 期刊: Statistics in Biopharmaceutical Research
• 影响因子: 1.8
• 作者: S. Calderazzo;S. Tarima;Carissa P. Reid;N. Flournoy;T. Friede;N. Geller;J. L. Rosenberger;N. Stallard;M. Ursino;M. Vandemeulebroecke;K. Van Lancker;S. Zohar

Using dichotomized survival data to construct a prior distribution for a Bayesian seamless Phase II/III clinical trial.

使用二分法生存数据来构建贝叶斯无缝II/III期临床试验的先前分布。

• DOI: 10.1177/09622802231160554
• 发表时间: 2023-05
• 期刊: STATISTICAL METHODS IN MEDICAL RESEARCH
• 影响因子: 2.3
• 作者: Duputel, Benjamin;Stallard, Nigel;Montestruc, Francois;Zohar, Sarah;Ursino, Moreno
• 通讯作者: Ursino, Moreno