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

@article{Sterne2016ROBINSIAT,
  title={ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions},
  author={Jonathan A. C. Sterne and Miguel A. Hern{\'a}n and Barnaby C. Reeves and Jelena Savovi{\'c} and Nancy D. Berkman and Meera Viswanathan and David Henry and Douglas G. Altman and Mohammed Toseef. Ansari and Isabelle Boutron and James R. Carpenter and An-Wen Chan and Rachel Churchill and Jonathan J Deeks and Asbj{\o}rn Hr{\~o}bjartsson and Jamie J. Kirkham and Peter J{\"u}ni and Yoon Kong Loke and Therese D. Pigott and Craig R. Ramsay and Deborah L. Regidor and Hannah R. Rothstein and Lakhbir Sandhu and Pasqualina Santaguida and Holger J. Schünemann and Beverly Shea and Ian Shrier and Peter Tugwell and Lucy Turner and Jeffrey C. Valentine and Hugh Waddington and Elizabeth Waters and George A Wells and Penny Whiting and Julian P. T. Higgins},
  journal={The BMJ},
  year={2016},
  volume={355}
}
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… Expand

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