Exploiting instrumental variables to estimate the effects of time-varying treatments using routine data

来自临床试验的证据在评估治疗的益处和危害方面发挥着核心作用，但对其中许多人来说，基于试验的证据是不可用的或不足的。政府机构越来越多地使用在医院、全科医生和疾病登记处常规收集的患者数据，以补充来自试验的证据。然而，使用常规数据的研究容易出现偏差，因为接受不同治疗的患者组之间存在观察到的和未观察到的差异。例如，不同治疗组的患者往往根据重要的预后变量而有所不同，这些变量可能是可测量的，也可能是不可测量的，这些变量既影响患者接受的治疗，也影响他们对治疗的反应。因此，政策制定者担心，在使用常规数据方面的这些潜在偏差可能会导致错误的治疗决定和糟糕的医疗资源分配。这项研究通过扩展一种被称为工具变量(IV)分析的方法来解决这些问题，该方法在社会科学中广泛用于解决观察到的和隐藏的偏差。IV分析本质上涉及使用变量(称为“工具”)，这些变量影响患者接受的治疗，但除了通过治疗本身之外，对健康结果没有影响。例如，IV分析经常使用遗传因素来估计风险因素对疾病的影响；基因与风险因素相关，但只通过风险因素影响健康结果，因为它们在出生时是随机分配的。因此，静脉注射方法可以在从常规数据中获得可靠证据方面发挥重要作用，但静脉注射方法在这些研究中的应用和有用性仍然知之甚少。特别是，政策制定者往往对评估随时间持续的治疗效果感兴趣，这需要在不同的时间点控制治疗组之间观察到的和未观察到的差异。现有的静脉注射方法不适合处理这种随时间变化的偏差。为了解决这些挑战，本研究的目标是：i)评估静脉注射分析在使用常规收集数据的研究中的有效性和有用性。特别是，这项研究将演示如何评估潜在工具的似是而非，以控制随着时间的推移产生的偏差。ii)在这方面解决IV方法实施中的分析问题。这将包括解决与仪器质量相关的问题，例如，这些变量对接受治疗的预测有多好。iii)说明拟议的静脉注射方法在不同临床环境中的灵活性和有效性；这些研究将包括评估类风湿性关节炎的生物治疗方法、2型糖尿病的强化血糖控制以及慢性心力衰竭的二线治疗。这项研究的结果将传播到学术界之外，例如，通过举办研讨会和实践讲习班，帮助应用研究人员、临床专家和政策分析人员了解如何使用常规收集的数据对长期维持的治疗战略的影响得出强有力的估计。这项研究还将把传播活动的重点放在那些直接参与分析和解释来自常规数据来源的证据的人身上，以便为资源分配决策提供信息，例如NICE科学、政策和研究顾问。通过帮助解决常规数据使用中观察到的和隐藏的偏差的主要问题，这项研究将有助于未来的研究提供更有力的证据，以最佳方式为治疗决策提供信息，以改善人群的健康。

Evidence from clinical trials plays a central role in the evaluation of the benefits and harms of treatments, but for many of them, trial-based evidence is unavailable or insufficient. Government agencies are increasingly using patient data collected routinely in hospitals, general practices, and disease registers, for complementing evidence from trials. However, studies that use routine data are prone to biases due to both observed and unobserved differences between patient groups receiving different treatments. For example, patients in different treatment groups often differ according to important prognostic variables, which may be measured or unmeasured, that affect both the treatment patients receive and how well they respond to treatment. Policy makers are therefore worried that these potential biases in the use of routine data may lead to wrong treatment decisions and poor allocation of healthcare resources. This research addresses these concerns by extending an approach, known as instrumental variables (IV) analysis, widely used in social sciences for addressing both observed and hidden biases. IV analysis essentially involves using variables (known as 'instruments') that affect the treatment patient receives but have no effect on health outcomes, except through the treatment itself. For example, IV analysis often uses genetic factors for estimating the effect of risk factors on disease; genes are associated with risk factors but only affect health outcomes through the risk factor because they are assigned at random at birth. IV methods can therefore play an important role in obtaining robust evidence from routine data, but the application and usefulness of IV methods in these studies remains poorly understood. In particular, policy makers are often interested in evaluating the effects of treatments sustained over time, which requires controlling for both observed and unobserved differences between treatment groups, at different points in time. Existing IV methods are not appropriate for addressing this type of biases that vary over time.In addressing these challenges, the objectives of this research are: i) to assess the validity and usefulness of IV analysis in studies that use routinely collected data. In particular, the research will demonstrate how to assess the plausibility of potential instruments to control for the biases over time.ii) to address analytical issues in the implementation of IV methods in this context. This will include addressing issues related to the quality of the instruments, for example, how well these variables predict the treatment received.iii) to illustrate the flexibility and usefulness of the proposed IV methods across different clinical settings; these will include studies evaluating biological treatments for rheumatoid arthritis, intensive glycaemic control for type 2 diabetes, and second-line treatments for chronic heart failure. The findings of this research will be disseminated beyond the academic community, for example, by delivering seminars and practical workshops to help applied researchers, clinical experts and policy analysts understand how derive robust estimates of the effects of treatment strategies sustained over time using routinely collected data. This research will also prioritise dissemination activities to those directly involved in the analysis and interpretation of evidence from routine data sources to inform resource allocation decisions, for example NICE Science, Policy and Research advisers. By helping address major concerns with both observed and hidden biases in the use of routine data, this research will help future studies provide more robust evidence to inform treatment decisions in the best ways for improving population's health.