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.

在建议对临床实践进行新的疗法之前，通常将在一项随机临床试验（RCT）中进行测试。 RCT是一项实验，将同意参与者随机分配给实验疗法和对照，这是当前的护理标准。传统上，RCT包括对照和单一的实验疗法，并在招募了目标参与者后进行单个分析。但是，就像在Covid-19大流行期间一样，可以同时使用多种实验疗法，在这种情况下，有效的设计是具有单个RCT，将同意的参与者分配给对照和可用的多种实验疗法。由于单个对照组用于所有实验治疗，因此与具有与不同的实验治疗相对应的单独的RCT相比，这节省了时间和其他资源。为了尽快做出重要的临床决策，例如，由于没有现有的有效治疗方法，因此在大流行期间所需的情况下，包括多次临时分析，以使能够早期从RCT中脱落是有益的，这些实验治疗方法是没有希望或早期结束的，因此某些实验治疗方法比对照更好。此外，新的实验治疗可能会在其他仍在测试中进行，并且将其添加到现有的RCT中是有效的。新的创新试验设计称为自适应平台试验，结合了这些效率方面。它们是有效的多臂多阶段RCT，其中评估了许多实验疗法。其中包括临时分析，有机会以积极的结果或徒劳的方式停止审判，放弃表现不佳的治疗方法或在试验中添加新的治疗方法。它们已被用于测试新疗法，包括英国的许多Covid-19 RCT中。进行统计分析时，就有机会得出不正确的结论。通过平台试验，有多种实例可以得出错误的结论。有多个临时分析，在每个分析中，都可以得出错误的结论。同样，在临时分析中，可以将试验中的多种实验治疗方法与选择继续进行进一步测试的方法进行比较，并且选择可能是偶然的。因此，适当的分析需要调整临时分析和决策的数量（适应），以便可以信心解释试验结果。该项目的目的是得出公式，以汇总平台试验的结果，同时在临时分析过程中调整试验适应。我们将重点放在得出量化实验处理临床益处的大小而不是对照的公式上，通常称为点和间隔估计器。重要的估计值是公正的，以避免错误地推荐临床实践的劣等治疗方法。平台试验后的计算估算公式未针对试验适应进行调整，因此可能给出有偏见的估计。衍生调整后的估计器很复杂。我们将基于已得出的估计器，以更简单的设置为II/III阶段RCT。我们还将考虑在实际平台试验中遇到的几种设置，例如测量治疗效果和不同适应性的不同方式，因此这将是一个很大的工作计划。该项目的预期输出是，在平台试验后，将清楚如何获得无偏见的估计。这将有助于增加平台试验的吸收。因此，与使用传统RCT相比，需要更快的人可以使用更好的疗法。