Jonathan AC Sterne , 1 , Miguel A Hernán 2 , Barnaby C Reeves 3 , Jelena Savović 1 , 4 , Nancy D Berkman 5 , Meera Viswanathan 6 , David Henry 7 , Douglas G Altman 8 , Mohammed T Ansari 9 , Isabelle Boutron 10 , James R Carpenter 11 , An-Wen Chan 12 , Rachel Churchill 13 , Jonathan J Deeks 14 , Asbjørn Hróbjartsson 15 , Jamie Kirkham 16 , Peter Jüni 17 , Yoon K Loke 18 , Theresa D Pigott 19 , Craig R Ramsay 20 , Deborah Regidor 21 , Hannah R Rothstein 22 , Lakhbir Sandhu 23 , Pasqualina L Santaguida 24 , Holger J Schünemann 25 , Beverly Shea 26 , Ian Shrier 27 , Peter Tugwell 28 , Lucy Turner 29 , Jeffrey C Valentine 30 , Hugh Waddington 31 , Elizabeth Waters 32 , George A Wells 33 , Penny F Whiting 34 , Julian PT Higgins 35
12 October 2016
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.