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

医学的进步使人们认识到，患者特征的差异可能导致患者对药物等疗法的反应不同。最近有很多医学研究兴趣的病人特征是那些基于基因组成的特征。例如，病人可以分为表达某种基因组成的人和不表达某种基因组成的人。通常使用的术语是生物标志物，表达某种基因组成的患者称为生物标志物阳性，不表达的患者称为生物标志物阴性。这个术语通常不局限于遗传学，因此可以用于，例如，将患者分为低于特定年龄的和高于特定年龄的。这个项目关注的是生物标记物可能与治疗相互作用的环境，也就是说，患者对治疗的反应取决于他们的生物标记物是阳性还是阴性。如果患者的反应取决于他/她的生物标志物是阳性还是阴性，则该生物标志物被称为预测性生物标志物。分层医学是医学的一个分支，其中新疗法的测试允许根据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。一种策略是分别进行两次试验。第一项试验包括来自整个人群的患者，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人群。第二项试验从选定的亚群中招募患者，其目的是利用收集到的数据对新疗法在该亚群中的效果（益处）进行明确的估计。一种有效的策略是进行单一试验，其中包括在试验中途进行中期分析，以选择从新疗法中受益的亚群。在中期分析之后，从选定的亚群中招募更多的患者，并使用他们的数据以及中期分析中使用的数据来确定新疗法在选定亚群中的效果大小。这种设计通常被称为自适应富集设计。它们是有效的，因为测试一种新疗法需要的病人更少。当使用适应性富集设计时，估计新疗法的效果大小需要根据用于选择亚群的临时数据也用于估计效果的事实进行调整。如果不这样做并使用标准方法，则效果将被高估，因为使用中期分析数据在选定的亚群中观察到的更大效果可能是偶然发生的。这是不可取的，因为对新疗法效果大小的明确估计被许多利益相关者用来决定是否采用一种疗法。只有一种适当的方法来估计治疗的效果，这是一个特定的设计，已经开发出适应性富集设计。这是使用这些设计的障碍之一。该项目的目的是通过开发新的方法来估计效应的大小，同时调整使用预测性生物标志物进行的亚种群选择，从而消除这一障碍。现有方法和这项工作之间的区别在于，我们将考虑不同形式的生物标志物，例如有多个生物标志物，我们还将考虑分层医学中常见类型的患者结果数据：时间到事件数据，如患者的总生存期。这项工作将增加适应性富集设计的使用。因此，这将在测试新疗法的同时节省资源，并导致更快速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.