HOD2: Instrumental Variable approaches for estimating heterogeneity of treatment effects to inform personalisation using electronic health records

个性化医疗旨在在正确的时间为正确的患者提供正确的治疗。告知个性化医疗的证据可以来自患者的电子健康记录。然而，使用这些数据来比较接受替代治疗的患者组的结果存在一些问题。首先，两组中的患者可能根据未测量的特征（例如，患者的虚弱）而不同。第二，干预导致患者结局（或伤害）改善的程度可能因这些不可测量的特征而异。第三，通常不知道哪些患者群体从哪些干预措施中受益。目前还没有解决这三个问题的方法。该项目将开发解决这些问题的新方法，并提供适用于个体患者的新治疗方法的有效性和危害的更准确估计。特别是，我们将开发新的方法，使更现实的假设。我们不需要假设我们知道哪些患者群体从哪种治疗中受益，我们将开发可以从数据中学习哪些亚组从哪种治疗中受益的方法。我们将通过测试这些新方法作为新研究的一部分，来检验它们在实践中的效果。其中一项研究将研究哪些患者从常见急性疾病（如阑尾炎）的紧急手术中受益，并使用150万次医院事件的数据。另一项研究考虑了哪些2型糖尿病患者受益于新的、更昂贵的口服治疗，并使用了来自25,000名患者的全科医学数据库的处方数据。我们的新方法将使我们能够提供更准确的相关证据，说明哪些干预措施最适合这两种疾病的患者。我们将为这些方法提供一个总体框架，可以应用于许多不同的疾病地区和国家。为了帮助未来的研究，我们将提供在不同背景下使用和适应这些方法的教程和指导。我们将举办短期课程和研讨会，以帮助那些设计，分析和解释使用电子健康记录的研究，为个别患者的治疗决策提供信息。

Personalised medicine aims to provide the right treatment for the right patient at the right time. The evidence to inform personalised medicine can come from patient's electronic health records. However, there are several problems with using these data to compare outcomes for groups of patients who receive alternative treatments. First, the patients in the two groups may differ according to characteristics that are not measured (for example, the patient's frailty). Second, the extent to which an intervention leads to an improvement in the patients' outcome (or harm) may differ according to these unmeasured characteristics. Third, it is often unknown which patient groups benefit from which interventions. Methods for addressing these three problems are currently unavailable. This project will develop new methods that resolve these problems and provide more accurate estimates of the effectiveness and harms of new treatments that apply to individual patients. In particular, we will develop new methods that make more realistic assumptions. Instead of assuming that we know which patient groups benefit from which treatment, we will develop approaches that can learn from the data about which subgroups benefit from which treatment. We will examine how well these new methods work in practice by testing them as part of new studies. One of these studies will examine which patients benefit from emergency surgery for common acute conditions (e.g. appendicitis), and uses data from 1.5 million hospital episodes. The other study considers which patients with type 2 Diabetes Mellitus benefit from new, more costly oral treatments, and uses prescription data from General Practice databases for 25,000 patients. Our new methods will enable us to provide more accurate, relevant evidence about which interventions work best for which patients with these two conditions. We will provide a general framework for these methods that can be applied across many different disease areas and countries. To help future studies, we will provide tutorials and guidance on using and adapting these methods in different contexts. We will run short courses and workshops to assist those designing, analysing and interpreting studies that use electronic health records to inform treatment decisions for individual patients.