HSM: Estimation of intervention effects for adaptive enrichment design RCTs that incorporate identification of predictive biomarkers

医学的进步已经使人们认识到，患者特征的差异可能导致患者对药物等疗法的反应差异。最近有很多医学研究兴趣的患者特征是基于遗传组成的。例如，患者可以被分类为表达某种遗传组成的患者和不表达某种遗传组成的患者。通常使用的术语是生物标志物，表达某种遗传组成的患者称为生物标志物阳性，而不表达的患者称为生物标志物阴性。该术语通常不限于遗传学，因此可以用于将患者分为特定年龄以下和特定年龄以上的患者。该项目关注生物标志物可能与治疗相互作用的环境，即患者对治疗的反应取决于他们是生物标志物阳性还是阴性。如果患者的反应取决于他/她是生物标志物阳性还是阴性，则该生物标志物被称为预测性生物标志物。分层医学是医学的分支，其中新疗法的测试允许通过生物标志物状态定义的不同亚群的治疗效果差异的可能性。一种策略是进行两次单独的审判。第一项试验由来自整个人群的患者组成，其目的是使用预测性生物标志物来选择将从新疗法中受益的亚群。第二项试验从选定的亚群中招募患者，其目的是使用收集的数据来确定新疗法在该亚群中的效果（获益）大小。一个有效的策略是进行一项单一试验，其中包括试验中途的中期分析，以选择从新疗法中获益的亚群。中期分析后，从选定的亚群中招募更多患者，并使用他们的数据以及中期分析中使用的数据，以确定新疗法在选定亚群中的效应大小。这种设计通常被称为自适应富集设计。当使用自适应富集设计时，估计新疗法的效果大小需要进行调整，因为用于选择亚群的中期数据也用于估计效果。如果不这样做并使用标准方法，则效应将被高估，因为使用中期分析数据在选定亚群中观察到的较大效应可能是偶然发生的。这是不可取的，因为许多利益相关者使用新疗法的效果大小的确定性估计来决定是否采用一种疗法。只有一种用于估计疗法效果的适当方法，并且用于特定设计，已经为适应性富集设计开发了。这是使用这些设计的障碍之一。本项目的目的是通过开发新的方法来消除这一障碍，该方法用于估计效应的大小，同时调整使用预测生物标志物进行的亚群选择。现有方法与本工作之间的区别在于，我们将考虑不同形式的生物标志物，例如具有一种以上的生物标志物，我们还将考虑分层医学中常见的患者结局数据类型：事件发生时间数据，如患者的总生存期。这项工作将增加自适应富集设计的使用。因此，这将在测试新疗法的同时节省资源，并导致更快地开发安全有效的治疗方法。

Advances in medicine have led to the acknowledgement that differences in patient characteristics may lead to differences in how patients respond to therapies such as drugs. Patient characteristics that have had much medical research interest recently are those based on the genetic make-up. For example, patients may be categorised as those who express a certain genetic make-up and those who do not. The term generally used is biomarker, with the patients who express a certain genetic make-up referred as being biomarker positive and those who do not referred as biomarker negative. The term is usually not restricted to genetics and so could be used, for example, to categorise patients into those below and those above a certain age.This project is concerned with settings where the biomarker may interact with a therapy, that is, patients' responses to a therapy depends on whether they are biomarker positive or negative. If a patient's response depends on whether he/she is biomarker positive or negative, the biomarker is said to be a predictive biomarker.Stratified medicine is the branch of medicine where testing of new therapies allows for the possibility of differences in therapy effects in different subpopulations defined by biomarker status. One strategy is to have two separate trials. The first trial consisting of patients from the full population and its aim is to use a predictive biomarker to select the subpopulation that will benefit from the new therapy. The second trial recruits patients from the selected subpopulation and its aim is to use the data collected to get a definitive estimate of the size of the effect (benefit) of the new therapy in this subpopulation.An efficient strategy is to have a single trial that includes an interim analysis partway through the trial to select the subpopulation that benefits from the new therapy. After the interim analysis, more patients are recruited from the selected subpopulation and their data, together with data used in the interim analysis, are used is to get a definitive estimate of the size of the effect of the new therapy in the selected subpopulation. Such designs are commonly referred to as adaptive enrichment designs. They are efficient because fewer patients would be required to test a new therapy.Estimating the size of the effect of the new therapy when an adaptive enrichment design is used needs to adjust for the fact that the interim data used to select the subpopulation are also used in estimating the effect. If this is not done and the standard methods are used, the effect will be overestimated because the larger effect observed in the selected subpopulation using the interim analysis data may have occurred by chance. This is undesirable because definitive estimates of the size of the effect of new therapies are used by many stakeholders to make a decision on whether to adopt a therapy.Only one appropriate method for estimating effects of therapies, and which is for a specific design, has been developed for adaptive enrichment designs. This is one of the barriers of using these designs. The aim of this project is to remove this barrier by developing new methods for estimating size of effects while adjusting for subpopulation selection made using a predictive biomarker.The difference between the existing method and this work is that we will consider different forms of biomarkers, such as having more than one biomarker, and we will also consider a common type of patient outcome data in stratified medicine: time to event data such as overall survival for patients.This work will increase the use of adaptive enrichment designs. Consequently, this will save resources while testing new therapies and lead to more rapid development of safe effective treatments.