GRADE guidelines: 18. How ROBINS-I and other tools to assess risk of bias in nonrandomized studies should be used to rate the certainty of a body of evidence

To provide guidance on how systematic review authors, guideline developers, and health technology assessment practitioners should approach the use of the risk of bias in nonrandomized studies of interventions (ROBINS-I) tool as a part of GRADE's certainty rating process. The study design and se...

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Published in:	Journal of clinical epidemiology Vol. 111; pp. 105 - 114
Main Authors:	Schünemann, Holger J., Cuello, Carlos, Akl, Elie A., Mustafa, Reem A., Meerpohl, Jörg J., Thayer, Kris, Morgan, Rebecca L., Gartlehner, Gerald, Kunz, Regina, Katikireddi, S Vittal, Sterne, Jonathan, Higgins, Julian PT, Guyatt, Gordon
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.07.2019 Elsevier Limited
Subjects:	Bias Certainty of the evidence Clinical trials Clinical Trials as Topic - standards Decision making Design Domains Dose-response effects Epidemiology Evidence-Based Medicine - methods Evidence-Based Medicine - standards GRADE Humans Internal Medicine Nonrandomized studies Observational Studies as Topic - standards Practice Guidelines as Topic - standards Quality Quality of evidence Risk Assessment Risk Factors Risk of bias ROBINS Studies Systematic Reviews as Topic Technology assessment Uncertainty Working groups GRADE Risk of bias ROBINS Quality of evidence Nonrandomized studies Certainty of the evidence
ISSN:	0895-4356, 1878-5921, 1878-5921
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Abstract	To provide guidance on how systematic review authors, guideline developers, and health technology assessment practitioners should approach the use of the risk of bias in nonrandomized studies of interventions (ROBINS-I) tool as a part of GRADE's certainty rating process. The study design and setting comprised iterative discussions, testing in systematic reviews, and presentation at GRADE working group meetings with feedback from the GRADE working group. We describe where to start the initial assessment of a body of evidence with the use of ROBINS-I and where one would anticipate the final rating would end up. The GRADE accounted for issues that mitigate concerns about confounding and selection bias by introducing the upgrading domains: large effects, dose-effect relations, and when plausible residual confounders or other biases increase certainty. They will need to be considered in an assessment of a body of evidence when using ROBINS-I. The use of ROBINS-I in GRADE assessments may allow for a better comparison of evidence from randomized controlled trials (RCTs) and nonrandomized studies (NRSs) because they are placed on a common metric for risk of bias. Challenges remain, including appropriate presentation of evidence from RCTs and NRSs for decision-making and how to optimally integrate RCTs and NRSs in an evidence assessment.
AbstractList	ObjectiveTo provide guidance on how systematic review authors, guideline developers, and health technology assessment practitioners should approach the use of the risk of bias in nonrandomized studies of interventions (ROBINS-I) tool as a part of GRADE's certainty rating process.Study Design and SettingThe study design and setting comprised iterative discussions, testing in systematic reviews, and presentation at GRADE working group meetings with feedback from the GRADE working group.ResultsWe describe where to start the initial assessment of a body of evidence with the use of ROBINS-I and where one would anticipate the final rating would end up. The GRADE accounted for issues that mitigate concerns about confounding and selection bias by introducing the upgrading domains: large effects, dose-effect relations, and when plausible residual confounders or other biases increase certainty. They will need to be considered in an assessment of a body of evidence when using ROBINS-I.ConclusionsThe use of ROBINS-I in GRADE assessments may allow for a better comparison of evidence from randomized controlled trials (RCTs) and nonrandomized studies (NRSs) because they are placed on a common metric for risk of bias. Challenges remain, including appropriate presentation of evidence from RCTs and NRSs for decision-making and how to optimally integrate RCTs and NRSs in an evidence assessment. To provide guidance on how systematic review authors, guideline developers, and health technology assessment practitioners should approach the use of the risk of bias in nonrandomized studies of interventions (ROBINS-I) tool as a part of GRADE's certainty rating process. The study design and setting comprised iterative discussions, testing in systematic reviews, and presentation at GRADE working group meetings with feedback from the GRADE working group. We describe where to start the initial assessment of a body of evidence with the use of ROBINS-I and where one would anticipate the final rating would end up. The GRADE accounted for issues that mitigate concerns about confounding and selection bias by introducing the upgrading domains: large effects, dose-effect relations, and when plausible residual confounders or other biases increase certainty. They will need to be considered in an assessment of a body of evidence when using ROBINS-I. The use of ROBINS-I in GRADE assessments may allow for a better comparison of evidence from randomized controlled trials (RCTs) and nonrandomized studies (NRSs) because they are placed on a common metric for risk of bias. Challenges remain, including appropriate presentation of evidence from RCTs and NRSs for decision-making and how to optimally integrate RCTs and NRSs in an evidence assessment. AbstractObjectiveTo provide guidance on how systematic review authors, guideline developers, and health technology assessment practitioners should approach the use of the risk of bias in nonrandomized studies of interventions (ROBINS-I) tool as a part of GRADE's certainty rating process. Study Design and SettingThe study design and setting comprised iterative discussions, testing in systematic reviews, and presentation at GRADE working group meetings with feedback from the GRADE working group. ResultsWe describe where to start the initial assessment of a body of evidence with the use of ROBINS-I and where one would anticipate the final rating would end up. The GRADE accounted for issues that mitigate concerns about confounding and selection bias by introducing the upgrading domains: large effects, dose-effect relations, and when plausible residual confounders or other biases increase certainty. They will need to be considered in an assessment of a body of evidence when using ROBINS-I. ConclusionsThe use of ROBINS-I in GRADE assessments may allow for a better comparison of evidence from randomized controlled trials (RCTs) and nonrandomized studies (NRSs) because they are placed on a common metric for risk of bias. Challenges remain, including appropriate presentation of evidence from RCTs and NRSs for decision-making and how to optimally integrate RCTs and NRSs in an evidence assessment. To provide guidance on how systematic review authors, guideline developers, and health technology assessment practitioners should approach the use of the risk of bias in nonrandomized studies of interventions (ROBINS-I) tool as a part of GRADE's certainty rating process.OBJECTIVETo provide guidance on how systematic review authors, guideline developers, and health technology assessment practitioners should approach the use of the risk of bias in nonrandomized studies of interventions (ROBINS-I) tool as a part of GRADE's certainty rating process.The study design and setting comprised iterative discussions, testing in systematic reviews, and presentation at GRADE working group meetings with feedback from the GRADE working group.STUDY DESIGN AND SETTINGThe study design and setting comprised iterative discussions, testing in systematic reviews, and presentation at GRADE working group meetings with feedback from the GRADE working group.We describe where to start the initial assessment of a body of evidence with the use of ROBINS-I and where one would anticipate the final rating would end up. The GRADE accounted for issues that mitigate concerns about confounding and selection bias by introducing the upgrading domains: large effects, dose-effect relations, and when plausible residual confounders or other biases increase certainty. They will need to be considered in an assessment of a body of evidence when using ROBINS-I.RESULTSWe describe where to start the initial assessment of a body of evidence with the use of ROBINS-I and where one would anticipate the final rating would end up. The GRADE accounted for issues that mitigate concerns about confounding and selection bias by introducing the upgrading domains: large effects, dose-effect relations, and when plausible residual confounders or other biases increase certainty. They will need to be considered in an assessment of a body of evidence when using ROBINS-I.The use of ROBINS-I in GRADE assessments may allow for a better comparison of evidence from randomized controlled trials (RCTs) and nonrandomized studies (NRSs) because they are placed on a common metric for risk of bias. Challenges remain, including appropriate presentation of evidence from RCTs and NRSs for decision-making and how to optimally integrate RCTs and NRSs in an evidence assessment.CONCLUSIONSThe use of ROBINS-I in GRADE assessments may allow for a better comparison of evidence from randomized controlled trials (RCTs) and nonrandomized studies (NRSs) because they are placed on a common metric for risk of bias. Challenges remain, including appropriate presentation of evidence from RCTs and NRSs for decision-making and how to optimally integrate RCTs and NRSs in an evidence assessment.
Author	Gartlehner, Gerald Guyatt, Gordon Mustafa, Reem A. Thayer, Kris Katikireddi, S Vittal Sterne, Jonathan Meerpohl, Jörg J. Higgins, Julian PT Cuello, Carlos Akl, Elie A. Schünemann, Holger J. Kunz, Regina Morgan, Rebecca L.
Author_xml	– sequence: 1 givenname: Holger J. surname: Schünemann fullname: Schünemann, Holger J. email: schuneh@mcmaster.ca organization: Department of Health Research Methods, Evidence, and Impact and McGRADE Center, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S4K1, Canada – sequence: 2 givenname: Carlos orcidid: 0000-0002-1742-0242 surname: Cuello fullname: Cuello, Carlos organization: Department of Health Research Methods, Evidence, and Impact and McGRADE Center, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S4K1, Canada – sequence: 3 givenname: Elie A. orcidid: 0000-0002-3444-8618 surname: Akl fullname: Akl, Elie A. organization: Department of Health Research Methods, Evidence, and Impact and McGRADE Center, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S4K1, Canada – sequence: 4 givenname: Reem A. surname: Mustafa fullname: Mustafa, Reem A. organization: Department of Health Research Methods, Evidence, and Impact and McGRADE Center, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S4K1, Canada – sequence: 5 givenname: Jörg J. orcidid: 0000-0002-1333-5403 surname: Meerpohl fullname: Meerpohl, Jörg J. organization: Cochrane Germany, Medical Center University of Freiburg, Breisacher Strasse 153, Freiburg, 79110, Germany – sequence: 6 givenname: Kris surname: Thayer fullname: Thayer, Kris organization: Integrated Risk Information System (IRIS) Division, National Center for Environmental Assessment, Environmental Protection Agency, 1200 Pennsylvania Avenue, N.W.Washington, DC 20460, USA – sequence: 7 givenname: Rebecca L. surname: Morgan fullname: Morgan, Rebecca L. organization: Department of Health Research Methods, Evidence, and Impact and McGRADE Center, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S4K1, Canada – sequence: 8 givenname: Gerald surname: Gartlehner fullname: Gartlehner, Gerald organization: Department for Evidence-Based Medicine and Clinical Epidemiology, Danube University Krems, Dr Karl Dorrek Straße 30, Krems, 3500, Austria – sequence: 9 givenname: Regina surname: Kunz fullname: Kunz, Regina organization: Basel Institute of Clinical Epidemiology, University Hospital Basel, Hebelstrasse 10, Basel, 4031, Switzerland – sequence: 10 givenname: S Vittal orcidid: 0000-0001-6593-9092 surname: Katikireddi fullname: Katikireddi, S Vittal organization: MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Top Floor, 200 Renfield Street, Glasgow, G2 3QB, Scotland – sequence: 11 givenname: Jonathan orcidid: 0000-0001-8496-6053 surname: Sterne fullname: Sterne, Jonathan organization: Population Health Sciences, Bristol Medical School, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK – sequence: 12 givenname: Julian PT surname: Higgins fullname: Higgins, Julian PT organization: Population Health Sciences, Bristol Medical School, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK – sequence: 13 givenname: Gordon surname: Guyatt fullname: Guyatt, Gordon organization: Department of Health Research Methods, Evidence, and Impact and McGRADE Center, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S4K1, Canada
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SubjectTerms	Bias Certainty of the evidence Clinical trials Clinical Trials as Topic - standards Decision making Design Domains Dose-response effects Epidemiology Evidence-Based Medicine - methods Evidence-Based Medicine - standards GRADE Humans Internal Medicine Nonrandomized studies Observational Studies as Topic - standards Practice Guidelines as Topic - standards Quality Quality of evidence Risk Assessment Risk Factors Risk of bias ROBINS Studies Systematic Reviews as Topic Technology assessment Uncertainty Working groups
Title	GRADE guidelines: 18. How ROBINS-I and other tools to assess risk of bias in nonrandomized studies should be used to rate the certainty of a body of evidence
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