HOD1: Comparative Effectiveness Research using Observational Data:Methodological Developments and a Roadmap (CER-OBS)

HOD1：使用观察数据的比较有效性研究：方法发展和路线图（CER-OBS）

• 批准号: MR/R025215/1
• 负责人: Bianca De Stavola
• 金额: $ 74.15万
• 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: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FR025215%2F1
• 关键词: HOD1; Comparative; Effectiveness; Research; using

There is increased availability of linked coded patient data that are collected in the course of clinical care and held electronically in formats and within safe environments that protect their anonymity. This creates unique opportunities for research into the benefits and harms of treatments when prescribed in "real-world" clinical practice ", as opposed to those identified (or not) when tested in clinical trials (which may involve limited numbers and/or types of patients). Evidence generated from routine data is however not immune from controversy, because of the challenges posed by being based on information that is not collected for research purposes. Indeed, errors and incompleteness often affect these data, with the timing and frequency of their collection also potentially biasing their analysis. Our research proposal attempts to address these potential biases by adopting and extending a novel approach to the analysis of routine data. It consists of emulating the ideal clinical trial for the efficacy of a treatment using routinely collected data (the "emulate the target trial" (ETT) approach). To make this happen, the patient population, treatment strategy, follow-up procedures and disease assessment need to be identified from the routine data, with the effectiveness measure, that the target trial would pursue, specified. To do this explicitly is not straightforward. Indeed, there are several examples where the design of studies based on routine data has introduced bias, mostly via incorrect definitions of the patient population or treatment received. Also, studying the effectiveness of treatments to be sustained over time requires estimation of effects that measure the impact of adherence to treatment, and not just its initiation. This poses analytical challenges, usually addressed by the methods that are not necessarily the most suitable, in particularly when data quality issues are also to be addressed. For these reasons, we propose to:A) Create a roadmap for the assessment and generation of evidence of comparative effectiveness (or harms) formulated within the ETT framework.B) Provide easy access to the most flexible of the estimation approaches for comparative effectiveness, g-estimation, and extend it to address the challenges posed by data quality, including facilitating sensitivity analyses. C) Use exemplars from linked UK databases to illustrate the application of ETT to CER. The treatment regimens being examined are: intensive versus less intensive cardiovascular disease prevention and glycaemic control in type 2 diabetes patients; antibiotics in infancy and subsequent asthma risk in childhood; and palivizumab, a monoclonal antibody, in high risk infants and later hospitalization due to bronchiolitis.These exemplars will illustrate the advantages of implementing the ETT approach when comparing treatment regimens in terms of estimating their benefits and harms (e.g. in the first example preventing heart disease but potentially increasing hypoglycaemic episodes). In comparison to more traditional uses of routine clinical data, ETT has greater transparency of purpose (addressing the same question as the target trial), which acts as a guide for the study design and analysis. The ability to 'enrol' large numbers of patients who receive their treatment in the "real-world", as delivered by the NHS, and who are followed for many years, as opposed to a limited period, is the great advantage of this approach over equivalent (i.e. pragmatic) randomised clinical trials. Overall this research will provide tools to aid users of research results (e.g. NICE) in assessing the quality of the available evidence from observational studies, and to guide applied researchers in the implementation of the ETT framework, including designing the study that corresponds to the clinical question, and explicitly dealing with data quality issues while adopting flexible and robust estimation approaches.

在临床护理过程中收集的链接编码的患者数据的可用性增加，并以保护其匿名性的安全环境中以电子方式持有。这为在“现实世界“临床实践”中规定的治疗的益处和伤害进行研究创造了独特的机会，而不是在临床试验中进行测试的那些（或未发现）（这可能涉及有限数量和/或类型的患者）。但是，从常规数据产生的证据和/或常规数据产生的证据并非受到争议的影响，因为这些挑战是基于这些挑战的挑战，而这些挑战的构成了这些挑战，而这些挑战是错误的，并且这些挑战是错误的。随着其收集的时间和频率，我们的研究提案可能会偏向于这些潜在的偏见。从常规数据（具有有效性衡量标准）中，指定了目标试验。明确执行此操作并不简单。实际上，在几个例子中，基于常规数据的研究设计引入了偏见，主要是通过对患者人群或接受治疗的定义不正确的定义。同样，研究治疗的有效性会随着时间的流逝而维持的，还需要估计衡量遵守治疗影响的影响，而不仅仅是其启动。这提出了分析挑战，通常由不一定最合适的方法解决，特别是在还需要解决数据质量问题的情况下。由于这些原因，我们建议：a）创建一个路线图，以评估和生成ETT框架内提出的比较有效性（或危害）的证据。B）可轻松访问估计方法最灵活的比较有效性，g-估计，并扩展其以应对数据质量所带来的挑战，包括提高敏感性分析，以应对挑战。 c）使用链接的英国数据库中的示例来说明ETT对CER的应用。所检查的治疗方案是：2型糖尿病患者的强化与不太强化心血管疾病预防和血糖控制；婴儿期的抗生素和随后的儿童哮喘风险；和一种单克隆抗体的帕利维珠单抗，在高风险婴儿中，后来由于支气管炎引起的住院治疗。这些示例将说明在比较治疗方案时以估计其益处和伤害的治疗方案时实施ETT方法的优势（例如，在第一个示例中，可以预防心脏病，但可能会增加较高的降低性下降疾病。）。与常规临床数据的传统用途相比，ETT具有更大的目的（解决与目标试验相同的问题），该目的是研究设计和分析的指南。 NHS提供的“注册”大量在“现实世界”中接受治疗的患者的能力是这种方法的最大优势，而不是等效的（即实用）随机临床试验，这是这种方法的最大优势。总体而言，这项研究将提供工具，以帮助研究结果的用户（例如NICE）评估观察性研究的可用证据质量，并指导应用研究人员实施ETT框架，包括设计与临床问题相对应的研究，并明确处理数据质量问题，同时采用灵活和强大的估计方法。

项目成果

Formulating causal questions and principled statistical answers.

• DOI: 10.1002/sim.8741
• 发表时间: 2020-12-30
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Goetghebeur E;le Cessie S;De Stavola B;Moodie EE;Waernbaum I;“on behalf of” the topic group Causal Inference (TG7) of the STRATOS initiative
• 通讯作者: “on behalf of” the topic group Causal Inference (TG7) of the STRATOS initiative

gesttools: General Purpose G-Estimation in R

gesttools：R 中的通用 G 估计

• 发表时间: 2022
• 期刊: Observational Studies
• 作者: Tompsett D

Access to palivizumab among children at high risk of respiratory syncytial virus complications in English hospitals.

英国医院呼吸道合胞病毒并发症高危儿童获得帕利珠单抗。

• DOI: 10.1111/bcp.15069
• 发表时间: 2022
• 期刊: British journal of clinical pharmacology
• 影响因子: 3.4
• 作者: Zylbersztejn A

Target Trial Emulation and Bias Through Missing Eligibility Data: An Application to a Study of Palivizumab for the Prevention of Hospitalization Due to Infant Respiratory Illness.

• DOI: 10.1093/aje/kwac202
• 发表时间: 2023-04-06
• 期刊: American journal of epidemiology
• 影响因子: 5

Framing Causal Questions in Life Course Epidemiology

• DOI: 10.1146/annurev-statistics-040120-024748
• 发表时间: 2022-01-01
• 期刊: ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION
• 影响因子: 7.9
• 作者: De Stavola, Bianca L.;Herle, Moritz;Pickles, Andrew
• 通讯作者: Pickles, Andrew