Emulating biomarker-guided target trials using big data

分层用药有可能提高治疗的获益风险比，而随机对照试验（RCT）是证明干预措施临床效用的金标准，包括生物标志物指导（BM指导）的治疗方法。许多BM引导的试验设计已经提出了这个目的，正如我们的小组在以前的工作中所确定的那样，这导致了我们基于网络的工具，用于BM引导的试验的设计和分析，BiGTeD 3。然而，BM引导的试验中感兴趣的结果通常是罕见的，生物标志物本身也可能是罕见的。这两个问题都意味着需要大而无法实现的样本量才能实现足够有效的随机对照试验。此外，进行随机对照试验可能需要多年时间，使其在快速发展的领域（如分层医学）中成为一个没有吸引力的选择。由于这些局限性，转向观察数据来证明临床效用似乎很有吸引力。这些数据可以来自更传统的病例对照或队列研究，结合荟萃分析可以提供与RCTs 5相当的效果的精确估计。最近，还可以从例行收集的来源获得观察数据，如电子健康记录，这些数据也可能与遗传数据和其他数据相关联。这些数据可在英国生物库（UKBB）6中获得。除了成本较低外，观察性研究可以产生更能代表基础患者的数据，而不存在RCT固有的一些条件和限制。此外，它们在RCT被认为是不道德的情况下是有用的，例如在缺乏临床均衡的情况下7。尽管观察数据有许多好处，但一个主要的限制是控制不可测量的混杂和其他偏差。然而，经过仔细考虑，从观察数据中推断因果效应可以通过旨在复制“理想RCT”来实现，否则我们将用于解决我们感兴趣的问题。这一过程通常被称为“模拟”“目标试验”8;在分层医学的背景下，“目标试验”将是BM指导的试验。基于临床特征模拟个性化治疗方法的目标试验的例子是可用的9，然而，考虑到具有患者治疗史和遗传和其他生物标志物数据的大型数据库的可用性，探索模拟目标试验如何在BM指导的试验领域中有用似乎是明智的。奖学金将涵盖哪些内容首先，将对文献进行回顾和评估，了解目前如何使用观察数据来评估BM指导治疗的临床实用性。接下来，将审查使用观察数据模拟目标试验的方法和指南，并适当考虑这些方法和指南在BM指导的试验环境中的适用性。将制定从观察数据中模拟BM指导试验的方法和指南，并将这些方法应用于从UKBB获得的真实的数据，以模拟BM指导治疗方法的试验。我们提出的范例是一项目标试验，测试遗传生物标志物SLCO 1B 1 *5在使用UKBB数据定制他汀类药物治疗中的临床效用。虽然他汀类药物是常用的，通常耐受性良好，但它们与他汀类药物相关的肌肉毒性（SRM）有关，范围从轻度到罕见，但可延长生命10。重要的是，SRM不仅会对患者造成直接伤害，还会导致他汀类药物停药和不依从，增加重大心血管事件和死亡率的风险11。已发现SLCO 1B 1 *5的携带者服用辛伐他汀而不是其他他汀类药物会显著增加SRM的风险，SLCO 1B 1 *5的检测为基于遗传学定制他汀类药物治疗提供了机会。Peyser等12进行了一项SLCO 1B 1指导他汀类药物治疗的RCT，但未能显示基因型指导方法的获益。这可能有几个原因，

Stratified medicine has potential to improve the benefit-risk ratio of treatments, and the randomised controlled trial (RCT) is the gold standard for demonstrating the clinical utility of an intervention, including a biomarker-guided ('BM-guided') approach to treatment. Many BM-guided trial designs have been proposed for this purpose, as identified in previous work by our group1,2, which led to our web-based tool for design and analysis of BM-guided trials, BiGTeD3.However, outcomes of interest in a BM-guided trial are often rare, and the biomarker itself can be rare. Both issues mean that large, unachievable sample sizes are required to achieve a sufficiently powered RCT. In addition, conducting RCTs can take many years making them an unattractive choice in a rapidly advancing field such as stratified medicine 4. Due to these limitations, turning to observational data to demonstrate clinical utility seems appealing. Such data can come from more traditional case-control or cohort studies, which combined in a meta-analysis can provide precise estimates of effect comparable to RCTs5. More recently, observational data are also available from routinely collected sources, such as electronic health records, which may also be linked to genetic and other data. Such data are available in the UK Biobank (UKBB)6. In addition to being less costly, observational studies can produce data more representative of the underlying patient, in the absence of some of the conditions and constraints inherent to being part of an RCT. Further, they are useful in situations where an RCT would be considered unethical, e.g. in the absence of clinical equipoise7. Despite the many benefits of observational data, a major limitation is controlling for unmeasurable confounding and other biases. However, with careful consideration, inference of causal effects from observational data can be achieved by aiming to replicate the 'ideal RCT' we would otherwise use to address our question of interest. This process is often referred to as 'emulating' a 'target trial'8; in the context of stratified medicine the 'target trial' would be a BM-guided trial. Examples are available of emulating target trials of a personalised approach to treatment based on clinical characteristics9, however given the availability of large databases with data on both patient treatment history and genetic and other biomarkers, it appears sensible to explore how emulating a target trial c be useful in the field of BM-guided trials. What the studentship will encompassFirst the literature will be reviewed and appraised on how observational data are currently used to assess clinical utility of BM-guided treatment. Next, methodologies and guidelines for emulating target trials using observational data will be reviewed, with due consideration to how applicable these are in a BM-guided trial setting. Methods and guidelines for emulating BM-guided trials from observational data will be developed, and these methods applied to real data obtained from UKBB to emulate trials of a BM-guided approach to treatment. The exemplar we propose is a target trial testing clinical utility of a genetic biomarker, SLCO1B1*5, in tailoring statin therapy using UKBB data. Whilst statins are commonly used and generally well tolerated, they are associated with statin-related myotoxicity (SRM) ranging from mild to rare but life-threatening10. Importantly SRM not only causes direct harm to patients, but also leads to statin discontinuation and non-adherence, increasing risk of major cardiovascular events and mortality11. Carriers of SLCO1B1*5 have been found to be at significantly increased risk of SRM from taking simvastatin but not other statins, and testing for SLCO1B1*5 provides an opportunity for tailoring statin therapy based on genetics. Peyser et al12 undertook a RCT of SLCO1B1 guided statin therapy, but failed to show a benefit of a genotype-guided approach. There may be several reasons for this in