Cited by 923Meta-analysis and systematic reviewsClinical practice guidelines implementationAdvanced Causal Inference Techniques

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index; none of the 7 sources its claims cite is checked for support yet.

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

Open access

Abstract

Objective 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 non-randomized studies of interventions (ROBINS-I) tool as part of GRADE’s certainty rating process. Study Design and Setting Iterative discussions, testing in systematic reviews, presentation at GRADE working group meetings with feedback from the GRADE Working Group. Results 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. 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. Conclusions The use of ROBINS-I in GRADE assessments may allow for a better comparison of evidence from RCTs and NRS because they are placed on a common metric for risk of bias. Challenges remain that include appropriate presentation of evidence from RCTs and NRS for decision-making and how to optimally integrate RCTs and NRS in an evidence assessment.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.jclinepi.2018.01.012

Cites

27 of 27 references verified0 citation errors · 0 not checkable

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  • It may state two separate claims as one. 12
23 of 23
  • The paper relies on this without support.

    Randomization is the only way to fully protect against confounding, and confounding is always a concern in even the most rigorously conducted observational studies.

    “The separation of randomized and observational studies was primarily a result of recognition that randomization is the only way to fully protect against confounding (i.e. imbalance in prognostic factors between intervention and control groups), and that confounding is always a concern in even the most rigorously conducted observational studies.”
  • The paper relies on this without support.

    The potential for residual confounding in observational studies is large, and adjusted analysis provides limited protection against bias, which justified starting observational studies at low certainty.

    “The choice of starting observational studies at low rather than moderate or very low certainty followed intense discussion in the GRADE working group’s early days, and was based on the group’s assessment of the magnitude of the potential for residual confounding, and the limited protection against bias provided by adjusted analysis in observational studies.”
  • The paper relies on this without support.

    A high-quality randomized trial avoids confounding and other biases such as selection or information biases.

    “Such a trial avoids both confounding (through random allocation to interventions) as well as other sources of bias such as selection or information biases.”
  • The paper relies on this without support.

    ROBINS-I has been carefully developed by experienced investigators, tested, and begun to be validated.

    “ROBINS-I has undergone careful development by a large group of experienced investigators. It has been tested and scientists have begun to validate it, and experience will continue to accumulate.”
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Metadata

Authors
Schünemann, H. J., Cuello, C., Akl, E. A., Mustafa, R. A., Meerpohl, J. J., Thayer, K., Morgan, R. L., Gartlehner, G., Kunz, R., Katikireddi, S. V., Sterne, J., Higgins, J. P., & Guyatt, G.
Affiliations
  • McMaster University (CA)
  • University of Kansas Medical Center (US)
  • Universität für Weiterbildung Krems (AT)
  • University Hospital of Basel (CH)
  • MRC/CSO Social and Public Health Sciences Unit (GB)
  • University of Bristol (GB)
Corresponding author
Schünemann, Holger J.
Venue
Journal of Clinical Epidemiology
Publisher
Elsevier BV
Published
2019-07
Volume
111
Pages
105-114
ISSN
0895-4356 (ISSN-L)
OpenAlex
W2792308249
Cited by
911
References
27
Citations by year
2026: 133 · 2025: 146 · 2024: 125 · 2023: 118 · 2022: 114 · 2021: 103 · 2020: 121 · 2019: 34
FWCI
52.17
Type
journal article
Language
en
Locations
  • Journal of Clinical Epidemiology (publishedVersion)
  • PubMed (publishedVersion)
  • Bristol Research (University of Bristol) (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
  • edoc (University of Basel) (submittedVersion)
  • Explore Bristol Research (submittedVersion, cc-by-nc-nd)
Funders
  • National Institute for Health and Care Research (NF-SI-0611-10168, NF-SI-0616-10111, NF-SI-0617-10145)
  • Medical Research Council (MR/M025209/1, MC_UU_12017/13, MC_UU_12017/15)
Topics
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
ROBINS-I, risk of bias, non-randomized studies, GRADE, quality of evidence, residual confounding, selection bias, confounding, evidence evaluation
MeSH terms
Systematic Reviews as Topic, Clinical Trials as Topic, Humans, Risk Factors, Bias, Practice Guidelines as Topic, Risk Assessment, Evidence-Based Medicine, Uncertainty, Observational Studies as Topic

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