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, Alexandra S J McAleenan, Jelena Savović, Kate M Tilling, Jonathan A C Sterne, et al

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review


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.

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.

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
Early online date24 Mar 2024
Publication statusE-pub ahead of print - 24 Mar 2024

Bibliographical note

Publisher Copyright:
© 2024

Structured keywords

  • ICEP


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