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

  • Julian P. T. Higgins*
  • , Rebecca L. Morgan
  • , Andrew A. Rooney
  • , Kyla W. Taylor
  • , Kristina A. Thayer
  • , Raquel A. Silva
  • , Courtney Lemeris
  • , Elie A. Akl
  • , Thomas F. Bateson
  • , Nancy D. Berkman
  • , Barbara S. Glenn
  • , Asbjørn Hróbjartsson
  • , Judy S. LaKind
  • , Alexandra McAleenan
  • , Joerg J. Meerpohl
  • , Rebecca M. Nachman
  • , Julie E. Obbagy
  • , Annette O'Connor
  • , Elizabeth G. Radke
  • , Jelena Savović
  • Holger J. Schünemann, Beverley Shea, Kate Tilling, Jos Verbeek, Meera Viswanathan, Jonathan A. C. Sterne
*Corresponding author for this work
  • Population Health Sciences, Bristol, United Kingdom
  • University of Bristol
  • National Institute for Health Research Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
  • McMaster University
  • National Institutes of Health
  • United States Environmental Protection Agency
  • ICF
  • American University of Beirut
  • RTI International
  • University of Southern Denmark
  • LLC
  • University of Freiburg
  • Cochrane Germany, Cochrane Germany Foundation, Freiburg, Germany
  • United States Department of Agriculture
  • Michigan State University
  • University of Ottawa
  • NIHR Bristol Biomedical Research Centre, Bristol, UK
  • Health Data Research UK

Research output: Contribution to journalArticleAcademicpeer-review

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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.
Original languageEnglish
Article number108602
JournalEnvironment international
Volume186
DOIs
Publication statusPublished - 1 Apr 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Confounding
  • Environmental
  • Epidemiology
  • Exposure
  • Misclassification/measurement bias
  • Risk of bias
  • Selection bias

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