ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions

Jonathan Ac Sterne*, Miguel A. Hernán, Barnaby C. Reeves, Jelena Savović, Nancy D. Berkman, Meera Viswanathan, David Henry, Douglas G Altman, Mohammed T. Ansari, Isabelle Boutron, James R. Carpenter, An Wen Chan, Rachel Churchill, Jonathan Deeks, Asbjørn Hróbjartsson, Jamie Kirkham, Peter Jüni, Yoon K. Loke, Theresa D. Pigott, Craig R. RamsayDeborah Regidor, Hannah R. Rothstein, Lakhbir Sandhu, Pasqualina L. Santaguida, Holger J. Schünemann, Beverly Shea, Ian Shrier, Peter Tugwell, Lucy Turner, Jeffrey C. Valentine, Hugh Waddington, Elizabeth Waters, George A. Wells, Penny F. Whiting, Julian Pt Higgins

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

2020 Citations (Scopus)

Abstract

Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I ("Risk Of Bias In Non-randomised Studies-of Interventions"), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.

Original languageEnglish
Article numberi4919
JournalBMJ: British Medical Journal
Volume355
DOIs
Publication statusPublished - 2016
Externally publishedYes

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