Evaluating effects of complex treatments using large observational datasets: from population to person

研究治疗对健康结果的影响是卫生保健决策的基础。人们越来越认识到，来自疾病登记处和电子健康记录等来源的观察数据可以提供关于治疗效果的“真实”证据。观察数据的优势在于，与随机试验相比，它们包含了大量的个体，以及接受特定治疗的更多样化的个体集合。一个主要挑战是，根据观察数据对治疗效果的研究容易产生偏差，包括由于接受某种治疗的人往往比没有接受治疗的人病情更重，因此面临更高的不良健康后果风险(即混淆)。近年来发展了统计方法，这些方法能够使用观察数据来估计治疗的效果，同时避免这种偏差，这些方法被称为因果推断方法。大多数关于治疗效果的研究都集中在患者群体的平均效果上。相反，这项研究计划的重点是开发方法，使我们能够通过使用随着时间推移收集的观察数据，提供关于个人在不同治疗选择下的预期健康结果的更个性化信息。我们将使用这些方法来调查在四个健康领域使用的治疗效果的问题。这个项目有四个工作包。第一个重点是开发“反事实预测”的创新统计方法，指的是根据个人特征，如年龄和健康状况，对一个人在不同治疗选择下的结果风险进行预测。我们还将展示如何评估反事实预测的准确性，如果要将其用于实践，这是至关重要的。第二个工作包侧重于回答相关问题的方法，即在临床测量达到给定水平的情况下，何时开始特定治疗可能是最佳的，这被称为动态治疗战略。这些方法将用于解决囊性纤维化(CF)(工作包III)以及高血压、糖尿病和重症监护药物(工作包IV)的治疗效果问题。慢性阻塞性肺疾病是英国最常见的遗传性疾病，近年来，有很大一部分英国慢性阻塞性肺病患者引入了精准药物。这就提出了关于先前现有治疗的益处、针对哪些患者以及何时进行治疗的重要问题。我们将使用英国CF注册中心的数据，对不同治疗选择下的结果进行反事实预测，并调查动态治疗策略。通过另外三个项目，我们将使用大规模电子健康记录数据集将这些方法应用于其他卫生领域。这将包括利用临床实践研究数据链中的全科医生记录数据来调查(1)开始服用预防心血管疾病的降压药物的最佳时机，以及这是否取决于其他患者的特征，以及(2)患者的特征和治疗时机开始对服用二甲双胍的2型糖尿病患者的二线治疗的益处有何影响。我们还将使用伦敦大学学院医院收治的患者的高频数据，根据是否允许患者进入重症监护的选项，对术后患者的死亡风险进行个性化预测。总而言之，这个项目将开发一些方法，使我们能够了解不同的治疗决定对不同的人可能产生的影响。这最终将产生信息，使患者和他们的医生能够就他们的治疗方案做出更个性化的决定。

Studies of the effects of treatments on health outcomes are the basis of decision-making in health care. It is increasingly recognised that observational data from sources such as disease registries and electronic health records can provide 'real world' evidence about effects of treatments. The advantage of observational data is that they contain large numbers of individuals and a more diverse set of individuals taking a given treatment than would be included in a randomised trial. A major challenge is that studies of treatment effects based on observational data are prone to bias, including due to those receiving a given treatment tending to be more ill than those who do not, and therefore being at higher risk of adverse health outcomes (i.e. confounding). Statistical methods have been developed in recent years that enable use of observational data to estimate effects of treatments while avoiding such biases, and these are referred to as causal inference methods. Most studies of treatment effects focus on average effects for the patient population. This research programme focuses instead on developing methods that will allow us to provide more personalised information about what an individual's health outcomes would be expected to be under different treatment choices, by using observational data collected over time. We will use these methods to investigate questions about effects of treatments used in four health areas.There are four work packages in this project. The first focuses on development of innovative statistical methods for 'counterfactual prediction', which refers to making predictions about what a person's risk of an outcome would be under different treatment choices given their individual characteristics such as their age and health status. We will also show how to assess how accurate counterfactual predictions are, which is vital if they are to be used in practice. The second work package focuses on methods for answering the related question of when it may be best to start a particular treatment, in terms of when a clinical measurement reaches a given level, which is referred to as a dynamic treatment strategies. The methods will be applied to tackle questions about treatment effects in cystic fibrosis (CF) (work package III) and in hypertension, diabetes, and intensive care medicine (work package IV). CF is the most common inherited disease in the UK, and recent years have seen the introduction of precision medicines for a large proportion of the UK CF population. This raises important questions about the benefits of prior existing treatments, for which patients, and when. We will use data from the UK CF Registry to develop counterfactual predictions of outcomes under different treatment choices, and to investigate dynamic treatment strategies. Through three further projects we will apply the methods in other health areas using large-scale electronic health records data sets. These will include using GP records data from the Clinical Practice Research Datalink to investigate (1) the optimal timing of initiation of blood pressure medications for preventing cardiovascular disease and whether this depends on other patient characteristics, and (2) how patient characteristics and timing of treatment start impact on benefits of second-line treatments for people with type 2 diabetes who have been prescribed Metformin. We will also use high-frequency data on patients admitted to University College London Hospitals to develop personalised predictions of risk of death for post-operative patients under the options of admitting the patient to intensive care or not. In summary, this project will develop methods that allow us to learn about what the impact of different treatment decisions could be for different people. This will ultimately result in information that allows patients and their doctors to make more personalised decisions about their treatment options.