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

在临床护理过程中收集的链接编码患者数据的可用性增加，并以电子形式保存在保护其匿名性的安全环境中。这为研究治疗的益处和危害创造了独特的机会，这与在临床试验(可能涉及有限数量和/或类型的患者)中测试时确定(或不确定)的治疗相反。然而，从常规数据产生的证据也不能幸免于争议，因为基于不是为研究目的收集的信息带来的挑战。事实上，错误和不完整经常影响这些数据，收集这些数据的时间和频率也可能影响他们的分析。我们的研究提案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差。它包括使用常规收集的数据来模拟治疗效果的理想临床试验(“模拟目标试验”(ETT)方法)。要做到这一点，需要从常规数据中确定患者群体、治疗策略、后续程序和疾病评估，并指定目标试验将追求的有效性衡量标准。要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见，主要是通过对患者群体或接受的治疗的错误定义。此外，研究长期持续治疗的有效性需要估计效果，以衡量坚持治疗的影响，而不仅仅是开始治疗。这带来了分析挑战，通常通过不一定是最合适的方法来解决，特别是在数据质量问题也要解决的情况下。出于这些原因，我们建议：a)为评估和生成在ETT框架内制定的相对有效性(或危害)证据制定一个路线图。b)提供比较有效性评估方法中最灵活的方法，即g-估计，并对其加以扩展，以应对数据质量带来的挑战，包括促进敏感性分析。C)使用链接的联合王国数据库中的样本来说明ETT在CER中的应用。正在研究的治疗方案包括：2型糖尿病患者的强化和非强化心血管疾病预防和血糖控制；婴儿时期的抗生素和随后的儿童哮喘风险；以及用于高危婴儿和后来因毛细支气管炎住院的单抗帕利维珠单抗。这些范例将说明在比较治疗方案的益处和危害时实施ETT方法的优势(例如，在第一个例子中，预防心脏病，但可能会增加低血糖发作)。与常规临床数据的更传统使用相比，ETT具有更大的目的透明度(解决与目标试验相同的问题)，作为研究设计和分析的指南。与同等(即实用的)随机临床试验相比，这种方法的巨大优势在于，能够“招募”大量患者，这些患者在NHS提供的“真实世界”中接受治疗，并接受多年的跟踪治疗，而不是有限的时期。总体而言，这项研究将提供工具，帮助研究结果的使用者(例如NICE)评估来自观察性研究的可用证据的质量，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，并在采用灵活而稳健的估计方法的同时明确处理数据质量问题。

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.