A tool to assess risk of bias in non-randomized follow-up studies of exposure effects (ROBINS-E)

Julian P. T. Higgins(University Hospitals Bristol NHS Foundation Trust), Rebecca L. Morgan(Impact), Andrew A. Rooney(Triangle), Kyla W. Taylor(Triangle), Kristina A. Thayer(Environmental Protection Agency), Raquel A. Silva(ICF International (United States)), Courtney Lemeris(ICF International (United States)), Elie A. Akl(American University of Beirut), Thomas F. Bateson(Environmental Protection Agency), Nancy D Berkman(RTI International), Barbara Glenn(Environmental Protection Agency), Asbjørn Hróbjartsson(University of Southern Denmark), Judy S. LaKind, Alexandra McAleenan(University of Bristol), Joerg J Meerpohl(University of Freiburg), Rebecca Nachman(Environmental Protection Agency), Julie Obbagy(Center for Nutrition Policy and Promotion), Annette M. O’Connor(Michigan State University), Elizabeth G. Radke(Environmental Protection Agency), Jelena Savović(University Hospitals Bristol NHS Foundation Trust), Holger J. Schünemann(Cochrane), Beverley Shea(Ottawa Hospital), Kate Tilling(University Hospitals Bristol NHS Foundation Trust), Jos Verbeek(Amsterdam UMC Location University of Amsterdam), Meera Viswanathan(RTI International), Jonathan A C Sterne(At Bristol)
Environment International
March 24, 2024
Cited by 770Open Access
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Abstract

BACKGROUND: Observational epidemiologic studies provide critical data for the evaluation of the potential effects of environmental, occupational and behavioural exposures on human health. Systematic reviews of these studies play a key role in informing policy and practice. Systematic reviews should incorporate assessments of the risk of bias in results of the included studies. OBJECTIVE: To develop a new tool, Risk Of Bias In Non-randomized Studies - of Exposures (ROBINS-E) to assess risk of bias in estimates from cohort studies of the causal effect of an exposure on an outcome. METHODS AND RESULTS: ROBINS-E was developed by a large group of researchers from diverse research and public health disciplines through a series of working groups, in-person meetings and pilot testing phases. The tool aims to assess the risk of bias in a specific result (exposure effect estimate) from an individual observational study that examines the effect of an exposure on an outcome. A series of preliminary considerations informs the core ROBINS-E assessment, including details of the result being assessed and the causal effect being estimated. The assessment addresses bias within seven domains, through a series of 'signalling questions'. Domain-level judgements about risk of bias are derived from the answers to these questions, then combined to produce an overall risk of bias judgement for the result, together with judgements about the direction of bias. CONCLUSION: ROBINS-E provides a standardized framework for examining potential biases in results from cohort studies. Future work will produce variants of the tool for other epidemiologic study designs (e.g. case-control studies). We believe that ROBINS-E represents an important development in the integration of exposure assessment, evidence synthesis and causal inference.


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