Welcome to the website for the ROBINS-E tool (Risk Of Bias In Non-randomized Studies - of Exposures)
We are pleased to make available a full version of ROBINS-E for follow-up studies.
A Word template is available for completing the tool.
The tool is also available in this ROBINS-E Excel implementation.
Observational epidemiologic studies are key to evaluation of the effects of exposures (including environmental, occupational and behavioural exposures) on human health, because evaluation through randomized controlled trials (RCTs) is not usually feasible. Even when RCTs have been conducted, the evidence that they provide may suffer from limitations. Therefore, the best evidence to guide policymakers and the public will be from observational studies that implement appropriate methods to minimize the risk of bias in their results, and from systematic reviews of such studies.
The Risk Of Bias In Non-randomized Studies - of Exposure (ROBINS-E) tool provides a structured approach to assessing the risk of bias in observational epidemiological studies. ROBINS-E is designed primarily for use in the context of a systematic review. It should contribute to a thorough examination of the strength of evidence about the presence of, and/or nature of, a potential effect of an exposure on an outcome. A key feature of the ROBINS-E approach is the specification, for each study, of the causal effect estimated by the result under consideration.
ROBINS-E shares many characteristics with the RoB 2 tool for randomized trials and the ROBINS-I tool for non-randomized studies of interventions, and is informing the further development of ROBINS-I. ROBINS-E includes seven domains of bias, each of which is addressed using a series of signalling questions that aim to gather important information about the study and the analysis being assessed. After the relevant signalling questions have been completed, three judgements are made:
1. The risk of bias in the result that arises from this domain.
2. The predicted direction of bias, balancing the various issues addressed within the domain.
3. Whether the risk of bias is sufficiently high to threaten conclusions about whether the exposure has an important effect on the outcome.
After completing all seven bias domains, an overall judgement is made for each of these three considerations.
We hope that the ROBINS-E tool will prove useful to the research community, and welcome constructive feedback by email to email@example.com.
ROBINS-E Development Group (Higgins J, Morgan R, Rooney A, Taylor K, Thayer K, Silva R, Lemeris C, Akl A, Arroyave W, Bateson T, Berkman N, Demers P, Forastiere F, Glenn B, Hróbjartsson A, Kirrane E, LaKind J, Luben T, Lunn R, McAleenan A, McGuinness L, Meerpohl J, Mehta S, Nachman R, Obbagy J, O'Connor A, Radke E, Savović J, Schubauer-Berigan M, Schwingl P, Schunemann H, Shea B, Steenland K, Stewart T, Straif K, Tilling K, Verbeek V, Vermeulen R, Viswanathan M, Zahm S, Sterne J). Risk Of Bias In Non-randomized Studies - of Exposure (ROBINS-E). Launch version, 20 June 2023. Available from: https://www.riskofbias.info/welcome/robins-e-tool.
ROBINS-E was developed by a large collaborative team.
Julian Higgins, Rebecca Morgan, Andrew Rooney, Kyla Taylor, Kris Thayer, Raquel Silva, Courtney Lemeris and Jonathan Sterne.
Elie Akl, Whitney Arroyave, Tom Bateson, Nancy Berkman, Paul Demers, Francesco Forastiere, Barbara Glenn, Asbjørn Hrobjartsson, Ellen Kirrane, Judy LaKind, Tom Luben, Ruth Lunn, Alexandra McAleenan, Luke McGuinness, Joerg Meerpohl, Suril Mehta, Rebecca Nachman, Julie Obbagy, Annette O'Connor, Beth Radke, Jelena Savovic, Mary Schubauer-Berigan, Pam Schwingl, Holger Schunemann, Bev Shea, Kyle Steenland, Trish Stewart, Kurt Straif, Kate Tilling, Jos Verbeek, Roel Vermeulen, Meera Viswanathan, Shelia Zahm.
We thank the following for contributions during early development of ROBINS-E: Robyn Blain, Carla Ancona, Mohammed Ansari, Abee Boyles, Carlos Cuello, Hannah Eglinton, Audrey Galizia, Ali Goldstone, Gordon Guyatt, David Henry, Miguel Hernán, Kevin Hobbie, Elizabeth Hodgson, Juleen Lam, Daniele Mandrioli, Inga Mills, Alessandria Schumacher, Chin Yang Shapland.
Logistics and meeting assistance for the Steering Committee and Development Group were supported in part by the Intramural Research Program (ES103316 and ES103317) at the National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health and performed for NIEHS under contract GS00Q14OADU417 (Order No. HHSN273201600015U).