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

利用常规数据利用工具变量来估计时变治疗的效果

基本信息

批准号：
MR/V020935/1
负责人：
Manuel De Oliveira Gomes
金额：
$ 59.87万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FV020935%2F1
关键词：
Exploiting instrumental variables estimate effects

项目摘要

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.

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，但对于其中许多人来说，基于试验的证据不可用或不足。政府机构越来越多地使用在医院、全科诊所和疾病登记处定期收集的患者数据，以补充试验证据。然而，由于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，使用常规数据的研究容易产生偏倚。例如，不同治疗组中的患者通常根据重要的预后变量而不同，这些变量可以是测量的或未测量的，其影响患者接受的治疗以及他们对治疗的反应如何。因此，政策制定者担心，常规数据使用中的这些潜在偏差可能导致错误的治疗决策和医疗资源分配不当。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，该方法广泛用于社会科学中，用于解决观察到的和隐藏的偏见。IV分析基本上涉及使用影响患者接受的治疗但对健康结果没有影响的变量（称为“工具”），除非通过治疗本身。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，IV方法可以在从常规数据中获得强有力的证据方面发挥重要作用，但IV方法在这些研究中的应用和有用性仍然知之甚少。特别是，决策者往往对评估长期持续的治疗效果感兴趣，这需要控制不同时间点治疗组之间观察到的和未观察到的差异。现有的IV方法不适合解决这种类型的偏差，随着时间的推移而变化。在解决这些挑战，本研究的目标是：i）评估IV分析的有效性和有用性的研究，使用常规收集的数据。特别是，该研究将展示如何评估潜在的工具，以控制随着时间的推移的偏差的可行性。ii）解决在这种情况下，在实施IV方法的分析问题。这将包括解决与仪器质量相关的问题，例如，这些变量如何预测接受的治疗。iii）说明在不同临床环境中拟议的IV方法的灵活性和有用性;这些将包括评估类风湿性关节炎生物治疗的研究，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以外，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持续的治疗策略的效果进行可靠的估计。这项研究还将优先向那些直接参与分析和解释常规数据来源证据的人传播活动，以告知资源分配决策，例如NICE科学，政策和研究顾问。通过帮助解决在使用常规数据时观察到的和隐藏的偏见的主要问题，这项研究将有助于未来的研究提供更有力的证据，以最佳方式为改善人群健康的治疗决策提供信息。