Emulating biomarker-guided target trials using big data

分层医学有可能提高治疗的效益-风险比，而随机对照试验(RCT)是证明干预的临床效用的黄金标准，包括生物标记物引导(BM引导)的治疗方法。为此目的，已经提出了许多BM指导的试验设计，正如我们小组1，2在之前的工作中所确定的那样，这导致了我们的基于网络的BM指导试验的设计和分析工具BiGTeD3。然而，在BM指导的试验中感兴趣的结果通常是罕见的，并且生物标记物本身也可能是罕见的。这两个问题意味着，要实现一个足够强大的随机对照试验，需要大量无法实现的样本量。此外，进行随机对照试验可能需要很多年的时间，这使得它们在分层医学等快速发展的领域中不再是一个有吸引力的选择。由于这些限制，求助于观察数据来证明临床实用似乎很有吸引力。这些数据可以来自更传统的病例对照或队列研究，结合在荟萃分析中可以提供与随机对照试验5相当的效果的准确估计。最近，还可以从常规收集的来源获得观测数据，例如电子健康记录，这些数据也可能与遗传和其他数据相关联。这样的数据可以在英国生物库(UKBB)6中获得。除了成本更低之外，观察性研究可以产生更能代表潜在患者的数据，而不需要一些条件和限制成为随机对照试验的一部分。此外，在随机对照试验被认为是不道德的情况下，例如在没有临床毒性7的情况下，它们是有用的。尽管观测数据有许多好处，但一个主要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，从观测数据中推断因果效应可以通过复制我们原本用来解决我们感兴趣的问题的“理想RCT”来实现。这一过程通常被称为“模仿”“靶标试验”8；在分层医学的背景下，“靶标试验”将是由BM指导的试验。可以提供基于临床特征的个体化治疗方法的模拟目标试验的例子9，然而，鉴于有关于患者治疗历史以及遗传和其他生物标记物的数据的大型数据库的可用性，探索模拟目标试验如何在BM指导的试验领域有用似乎是明智的。首先，将回顾和评价目前如何使用观察数据来评估BM引导治疗的临床实用性。下一步，将审查使用观测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。将制定根据观察数据模拟BM指导试验的方法和指南，并将这些方法应用于从UKBB获得的真实数据，以模拟BM指导治疗方法的试验。我们建议的样本是一项目标试验，测试遗传生物标记物SLCO1B1*5在使用UKBB数据定制他汀类药物治疗中的临床实用性。虽然他汀类药物被广泛使用且耐受性良好，但它们与他汀类药物相关的肌肉毒性(SRM)有关，范围从轻微到罕见，但威胁生命10。重要的是，SRM不仅对患者造成直接伤害，还会导致他汀类药物的停用和不坚持，增加发生重大心血管事件和死亡的风险11。SLCO1B1*5的携带者被发现服用辛伐他汀而不服用其他他汀类药物会显著增加SRM的风险，SLCO1B1*5的检测为基于遗传学的他汀类药物的定制治疗提供了机会。Peyser等人进行了SLCO1B1引导的他汀类药物治疗的随机对照试验，但未能显示出基因引导的方法的益处。造成这种情况的原因可能有几个

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