Efficient and unbiased estimation in adaptive platform trials

在一种新疗法被推荐用于临床实践之前，它通常已经在随机临床试验(RCT)中进行了测试。随机对照试验是将同意的参与者随机分配到试验性治疗和对照组，这是目前的护理标准。传统的随机对照试验包括对照组和单一的实验性治疗，在招募了目标数量的参与者后进行单一的分析。然而，就像新冠肺炎大流行期间的情况一样，可能会同时提供多种实验治疗，在这种情况下，一个有效的设计是有一个随机对照试验，将同意的参与者分配到对照和可用的多个实验治疗中。由于所有实验处理都使用一个控制臂，因此与不同实验处理对应的不同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定，例如在大流行期间由于没有现有有效的治疗方法而希望做出的决定，包括多项中期分析是有益的，以便能够及早从随机对照试验中丢弃没有希望的实验性治疗方法，或者及早得出一些实验性治疗方法优于对照的结论。此外，当其他治疗方法仍在测试中时，可能会出现新的实验性治疗方法，将它们添加到现有的随机对照试验中是有效的。被称为适应性平台试验的新的创新试验设计纳入了这些效率方面。它们是有效的多臂多阶段随机对照试验，在这些试验中对一些实验疗法进行了评估。它们包括中期分析，使试验有机会在结果积极或无效的情况下提前停止试验，放弃效果不佳的治疗，或在试验中增加新的治疗方法。它们已经被用于测试新的疗法，包括在英国的一些新冠肺炎随机对照试验中。无论何时进行统计分析，都有可能得出错误的结论。在平台试验中，有多个实例会得出错误的结论。有多个中期分析，在每一个分析中，都可能得出错误的结论。此外，在中期分析期间，可以比较试验中的多个实验治疗，以选择那些继续进行进一步测试的治疗，并且选择可能是偶然的。因此，适当的分析需要针对在中期分析(调整)时所做的中期分析和决定的数量进行调整，以便能够对试验结果进行可信的解释。该项目的目的是在中期分析期间根据试验调整来得出总结平台试验结果的公式。我们将专注于推导公式，量化实验治疗相对于对照的临床益处的大小，通常被称为点和区间估计器。重要的是，估计是公正的，以避免在临床实践中错误地推荐劣质的治疗方法。现有的计算平台试验后估计的公式不能根据试验调整进行调整，因此可能会给出有偏的估计。我们将建立在被称为第二/第三阶段随机对照试验的更为简单的设置的估计值的基础上。我们还将考虑实际平台试验中遇到的几个设置，例如测量治疗效果的不同方法和不同的适应，因此这将是一个巨大的工作计划。该项目的预期输出是，如何在平台试验后获得公正的估计将是清晰的。这将有助于增加对平台试验的接受。因此，与使用传统的随机对照试验相比，需要更好疗法的人将更快获得更好的疗法。

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.