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.

链接编码的患者数据的可用性越来越高，这些数据是在临床护理过程中收集的，并以电子形式保存，并在保护其匿名性的安全环境中保存。这为研究在“现实世界”临床实践中规定的治疗的益处和危害创造了独特的机会，而不是在临床试验中测试时确定（或未确定）的那些（可能涉及有限数量和/或类型的患者）。然而，从常规数据中产生的证据也不能免于争议，因为所依据的信息并非为研究目的而收集，这带来了挑战。事实上，错误和不完整往往会影响这些数据，收集数据的时间和频率也可能使数据分析产生偏差。我们的研究建议试图通过采用和扩展一种新的方法来分析常规数据，以解决这些潜在的偏见。它包括使用常规收集的数据模拟治疗效果的理想临床试验（“模拟目标试验”（ETT）方法）。为了实现这一目标，需要从常规数据中确定患者人群、治疗策略、随访程序和疾病评估，并指定目标试验将追求的有效性指标。明确地做到这一点并不简单。事实上，有几个例子表明，基于常规数据的研究设计引入了偏倚，主要是通过对患者人群或接受的治疗的不正确定义。此外，研究随着时间的推移而持续的治疗效果需要估计效果，以衡量坚持治疗的影响，而不仅仅是开始治疗。这就带来了分析方面的挑战，通常采用不一定是最合适的方法来解决，特别是在还需要解决数据质量问题的情况下。出于这些原因，我们建议：A）创建一个路线图，用于评估和生成在ETT框架内制定的相对有效性（或危害）的证据。B）提供最灵活的相对有效性估计方法g-估计的便捷访问，并将其扩展到解决数据质量带来的挑战，包括促进敏感性分析。C）使用链接的英国数据库中的示例来说明ETT在CER中的应用。正在研究的治疗方案是：2型糖尿病患者中强化与不强化的心血管疾病预防和血糖控制;婴儿期抗生素和随后的儿童哮喘风险;和帕利珠单抗，一种单克隆抗体，这些例子将说明在比较治疗方案时实施ETT方法在估计它们的益处和危害（例如，在第一个例子中，预防心脏病，但可能增加低血糖发作）。与常规临床数据的更传统用途相比，ETT具有更高的目的透明度（解决与目标试验相同的问题），可作为研究设计和分析的指南。能够“招募”大量在“现实世界”中接受治疗的患者，如NHS提供的，并且他们被随访多年，而不是有限的时间，这是这种方法相对于等效（即实用）随机临床试验的巨大优势。总的来说，这项研究将提供工具，帮助研究结果的用户（例如NICE）评估观察性研究中可用证据的质量，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，同时采用灵活和稳健的估计方法。

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.

通过缺失资格数据的目标试验仿真和偏见：对帕利维珠单抗研究的应用，以预防婴儿呼吸道疾病引起的住院治疗。

• 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