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

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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.