Efficient and unbiased estimation in adaptive platform trials

自适应平台试验中的高效且公正的估计

• 批准号: MR/X030261/1
• 负责人: Peter Kimani
• 金额: $ 55.88万
• 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: 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%2FX030261%2F1
• 关键词: Efficient; unbiased; estimation; adaptive; platform

Before a new therapy is recommended for clinical practice, it will usually have been tested in a randomised clinical trial (RCT). An RCT is an experiment that randomly allocates consented participants to the experimental therapy and to the control, which is the current standard of care. Traditionally RCTs include a control and a single experimental therapy, and a single analysis is performed after the target number of participants has been recruited. However, as was the case during COVID-19 pandemic, multiple experimental therapies may become available simultaneously in which case an efficient design is to have a single RCT that allocates consented participants to the control and the available multiple experimental treatments. Because a single control arm is used for all the experimental treatments, this saves time and other resources compared to having separate RCTs corresponding to different experimental treatments. To enable making important clinical decisions as quickly as possible, for example as was desired during the pandemic because there was no existing efficacious treatment, it is beneficial to include multiple interim analyses to enable dropping early from the RCT the experimental treatments that are not promising or to conclude early that some of the experimental treatments are superior to the control. Also, new experimental treatments may become available while others are still being tested and it is efficient to add them to an existing RCT. New innovative trial designs referred to as adaptive platform trials incorporate these efficiency aspects. They are efficient multi-arm multi-stage RCTs in which a number of experimental therapies are assessed. They include interim analyses, giving the opportunity to stop the trial early with a positive result or due to futility, to drop poorly performing treatments, or add new ones to the trial. They have been used to test new therapies including in a number of COVID-19 RCTs in the UK.Whenever a statistical analysis is performed, there is a chance to make an incorrect conclusion. With platform trials, there are multiple instances to make an incorrect conclusion. There are multiple interim analyses and in each, an incorrect conclusion can be made. Also, during interim analyses, the multiple experimental treatments in the trial may be compared to select those that continue with further testing and the selection may be by chance. Consequently, appropriate analysis needs to adjust for the number of interim analyses and decisions made at interim analysis (adaptations) so that the trial's results can be interpreted with confidence.The aim of this project is to derive formulas to summarise the results of a platform trial while adjusting for the trial adaptations during interim analyses. We will focus on deriving formulas that quantify the magnitude of the clinical benefits of experimental treatments over the control, commonly referred to as point and interval estimators. It is important estimates are unbiased to avoid erroneously recommending inferior treatments for clinical practice. The existing formulas for computing estimates following platform trials do not adjust for trial adaptations and so may give biased estimates.Deriving adjusted estimators is complex. We will build on estimators that have been derived for much simpler setting referred to as phase II/III RCTs. We will also consider several settings encountered in real platform trials such as different ways of measuring a treatment effect and different adaptations and so it will be a big programme of work.The expected output from the project is that it will be clear how to obtain unbiased estimates following platform trials. This will contribute to the increase in uptake of platform trials. Consequently, better therapies will become available to those who need them more quickly compared to using traditional RCTs.

在推荐一种新疗法用于临床实践之前，通常会在随机临床试验（RCT）中进行测试。随机对照试验（RCT）是一项实验，将同意的参与者随机分配到实验治疗和对照组，对照组是目前的护理标准。传统的随机对照试验包括对照和单一的实验性治疗，在招募目标数量的参与者后进行单一分析。然而，与COVID-19大流行期间的情况一样，多种实验性治疗可能同时可用，在这种情况下，有效的设计是采用单一RCT，将同意的受试者分配到对照组和可用的多种实验性治疗组。由于所有实验治疗均使用单一对照组，因此与对应于不同实验治疗的单独RCT相比，这节省了时间和其他资源。为了能够尽快做出重要的临床决策，例如，在大流行期间由于没有现有的有效治疗而期望的，包括多个中期分析是有益的，以能够从RCT中及早放弃没有希望的实验治疗或及早得出结论，一些实验治疗上级对照。此外，新的实验性治疗方法可能会在其他治疗方法仍在测试中的时候出现，将它们添加到现有的RCT中是有效的。被称为适应性平台试验的新的创新试验设计包含了这些效率方面。它们是有效的多组多阶段RCT，其中评估了许多实验性疗法。它们包括中期分析，提供机会提前停止试验，获得阳性结果或由于无效，放弃表现不佳的治疗，或在试验中添加新的治疗。它们已被用于测试新疗法，包括在英国的一些COVID-19 RCT中。每当进行统计分析时，都有可能得出错误的结论。对于平台试验，有多种情况可以得出错误的结论。有多项中期分析，每项分析都可能得出不正确的结论。此外，在中期分析期间，可能会比较试验中的多种实验治疗，以选择继续进行进一步检测的治疗，选择可能是偶然的。因此，适当的分析需要调整中期分析的数量和中期分析时做出的决定（调整），以便可以有信心地解释试验结果。本项目的目的是推导出总结平台试验结果的公式，同时调整中期分析期间的试验调整。我们将专注于推导公式，量化实验治疗的临床效益的大小，通常被称为点和区间估计。重要的是估计是无偏的，以避免错误地推荐临床实践中的劣效治疗。现有的计算平台试验后估计值的公式没有对试验适应性进行调整，因此可能会给出有偏估计值。我们将建立在已经为更简单的设置（称为II/III期RCT）推导出的估计值的基础上。我们还将考虑在真实的平台试验中遇到的几种设置，例如测量治疗效果的不同方法和不同的适应性，因此这将是一个大的工作计划。该项目的预期输出是，将清楚如何在平台试验后获得无偏估计。这将有助于增加平台试验的采用。因此，与使用传统RCT相比，更好的疗法将更快地提供给那些需要它们的人。