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.

在推荐新疗法用于临床实践之前，通常会在随机临床试验 (RCT) 中对其进行测试。随机对照试验是一种将同意的参与者随机分配到实验治疗和对照（这是当前的护理标准）的实验。传统上，随机对照试验包括对照和单一实验疗法，并且在招募目标数量的参与者后进行单一分析。然而，正如 COVID-19 大流行期间的情况一样，多种实验疗法可能同时可用，在这种情况下，有效的设计是采用单一 RCT，将同意的参与者分配给对照组和可用的多种实验疗法。由于所有实验处理均使用单个控制臂，因此与对应不同实验处理的单独 RCT 相比，这可以节省时间和其他资源。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有效的治疗方法而需要做出的决定，包括多个中期分析是有益的，以便能够尽早从 RCT 中放弃那些没有希望或没有希望的实验性治疗方法。尽早得出结论，某些实验处理优于对照。此外，当其他治疗方法仍在测试时，新的实验治疗方法可能会出现，将它们添加到现有的随机对照试验中是有效的。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随机对照试验，其中评估了许多实验疗法。其中包括中期分析，使试验有机会因阳性结果或无效而提前停止试验，放弃效果不佳的治疗方法，或在试验中添加新的治疗方法。它们已被用来测试新疗法，包括英国的许多 COVID-19 随机对照试验。每当进行统计分析时，都有可能得出错误的结论。通过平台试验，有多个实例可以得出错误的结论。有多个中期分析，每个分析都可能得出错误的结论。此外，在中期分析期间，可以对试验中的多个实验处理进行比较，以选择那些继续进行进一步测试的处理，并且该选择可能是偶然的。因此，适当的分析需要根据中期分析的数量和中期分析中做出的决策（适应）进行调整，以便可以自信地解释试验结果。该项目的目的是推导出公式来总结平台试验的结果同时在中期分析期间调整试验适应性。我们将重点推导量化实验治疗相对于对照的临床获益程度的公式，通常称为点估计和区间估计。重要的是，估计是公正的，以避免错误地推荐较差的治疗方法用于临床实践。平台试验后计算估计的现有公式不会根据试验适应进行调整，因此可能会给出有偏差的估计。得出调整后的估计量很复杂。我们将建立在为更简单的设置（称为 II/III 期 RCT）而导出的估计器的基础上。我们还将考虑在真实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，因此这将是一个很大的工作计划。该项目的预期输出是，将清楚如何获得公正的结果平台试验后的估计。这将有助于增加平台试验的采用率。因此，与传统的随机对照试验相比，更好的治疗方法将更快地提供给有需要的人。