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

Authors

Jonathan A.C. Sterne, University of Bristol
Miguel A. Hernán, Harvard University
Barnaby C. Reeves, University of Bristol
Jelena Savovic, University of Bristol
Nancy D. Berkman, RTI International
Meera Viswanathan, RTI International
David Henry, University of Toronto
Douglas G. Altman, University of Oxford
Mohammed T. Ansari, University of Ottawa
Isabelle Boutron, University Paris Descartes
James R. Carpenter, University College London
An-Wen Chen, University of Toronto
Rachel Churchill, University of York
Jonathan J. Deeks, University of Birmingham
Asbjørn Hróbjartsson, University of Southern Denmark
Jamie Kirkham, University of Liverpool
Peter Jüni, University of Toronto
Yoon K. Loke, University of East Anglia
Theresa D. Pigott, Loyola University Chicago
Craig R. Ramsay, University of Aberdeen
Deborah Regidor, Care Management Institute
Hannah R. Rothstein, CUNY Bernard M Baruch CollegeFollow
Lakhbir Sandhu, University of Toronto
Pasqualina L. Santaguida, McMaster University
Holger J. Schünemann, MacGRADE Center
Beverly Shea, University of Ottowa
Ian Shrier, McGill University
Peter Tugwell, University of Ottawa
Lucy Turner, Ottawa Hospital Research Institute
Jeffrey C. Valentine, University of Louisville
Hugh Waddington, London International Development Centre
Elizabeth Waters, University of Melbourne
George A. Wells, University of Ottawa
Penny F. Whiting, University of Bristol
Julian PT Higgins, University of Bristol

Document Type

Article

Publication Date

10-12-2016

Abstract

Non-randomized 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-randomized 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 randomization 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-randomized studies.

Comments

This article was originally published in BMJ, available at http://dx.doi.org/10.1136/bmj.i4919.

This article is an open access article published under a Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license.