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 accessAbstract
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.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We have not identified bodies of evidence in which a ROBINS-I assessment alone leads to no rating down, or rating down by only one level.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Summary and next steps
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In the paper
“In one of our prior articles we described that a systematic review of NRS investigating the effect of cyclooxygenase-2 inhibitors on cardiovascular events found that the summary estimate of RR with rofecoxib of 1.33 (95% CI: 1.00 to 1.79) with doses less than 25mg/d and 2.19 (95% CI: 1.64 to 2.91) with doses more than 25 mg/d.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > What makes us confident in results of NRS and does GRADE already account for this? > Large effects and dose responses
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Third, there is a possibility of misuse by those wanting to assign a higher certainty of the evidence to a body of evidence from NRS than is appropriate. Evaluators of evidence may rate the risk of bias from a group of NRS as moderate risk of bias if they are not appropriately cautious about the impact of confounding and selection bias or if reporting is poor.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Advantages and disadvantages of, in the context of GRADE, assessing risk of bias for individual studies using the ROBINS-I approach of specifying target trials > Disadvantages
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With regards to large effects, if a body of evidence from NRS is indeed rated as high-certainty in the evidence prior to the consideration of size of effect and very large effects exist, no further rating up is possible or required.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Unresolved Issues
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This approach implies that ROBINS-I users rating conventional NRS of any design (e.g. cohort, case-control) following their assessment of confounding and selection bias, will often arrive at a rating of high risk of bias.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Certainty of evidence for a body of evidence from NRS when using ROBINS-I for assessing risk of bias in individual NRS
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclear- Rests on an assumption: Randomization is the only way to fully protect against confounding, and confounding is always a concern in even the most rigorously conducted observational studies.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, when using ROBINS-I for assessing risk of bias in NRS, given that assessment of selection bias and confounding is an integral part of the ROBINS-I tool, the initial GRADE certainty in the evidence from a body of studies using an NRS design would be high (Figures 4 and 5).”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Certainty of evidence for a body of evidence from NRS when using ROBINS-I for assessing risk of bias in individual NRS
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“GRADE suggests that large effects and dose-response relations mitigate concerns regarding residual confounding.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > What makes us confident in results of NRS and does GRADE already account for this? > Large effects and dose responses
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“GRADE allows higher certainty ratings for bodies of evidence when all plausible residual confounders or other biases increase our confidence in the range of an estimated effect, that is the effect is either larger or smaller than that observed (23, 24).”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > What makes us confident in results of NRS and does GRADE already account for this? > All plausible residual confounders or other biases increase our certainty in the estimated effect
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“One example from the public health field comes from a systematic review of NRS including a total of 38 million patients that demonstrated a very small relative increase (relative risk 1.020, 95% confidence interval 1.003-1.038) in death rates in private for-profit compared with private not-for-profit hospitals (23, 25).”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > What makes us confident in results of NRS and does GRADE already account for this? > All plausible residual confounders or other biases increase our certainty in the estimated effect
What the sources say
- Not checked yetcites [23] Guyatt, 2011, GRADE guidelines: 9. Rating up the quality of evidence, J Clin Epidemiol source
- Not checked yetcites [25] Devereaux, 2002, A systematic review and meta-analysis of studies comparing mortality rates of private for-profit and private not-for-profit hospitals, CMAJ
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, ROBINS-I will facilitate assessment of a study that has been described as randomized but when assessed in detail is found to be not appropriately randomized.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Advantages and disadvantages of, in the context of GRADE, assessing risk of bias for individual studies using the ROBINS-I approach of specifying target trials > Advantages
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- Probably not a substantial claim. 15
- It may state two separate claims as one. 12
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Fifth, until now there is no practical example on which to base a rating of initial high or even moderate certainty in the evidence that comes from a body of evidence from NRS where no traditional GRADE upgrading domain applies.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Advantages and disadvantages of, in the context of GRADE, assessing risk of bias for individual studies using the ROBINS-I approach of specifying target trials > Disadvantages
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When raters use ROBINS-I with NRS beginning at high certainty, three levels for rating down for risk of bias are required so that NRS can arrive at a rating of very low certainty after considering risk of bias.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Unresolved Issues
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given the inherent limitations of studies that do not use randomization, a body of evidence from NRS studies should generally not lead to moderate or high certainty in the evidence in relation to risk of bias.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Summary and next steps
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Rests on an assumption: Randomization is the only way to fully protect against confounding, and confounding is always a concern in even the most rigorously conducted observational studies.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using ROBINS-I it nevertheless remains possible that a body of evidence from NRS studies will receive a final rating of high or moderate certainty of evidence. This could result from rating up for large effect, dose-response, or the direction of plausible confounding. Or it could result from use of NRS designs and analyses with greater protection against risk of bias – for instance, interrupted”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Certainty of evidence for a body of evidence from NRS when using ROBINS-I for assessing risk of bias in individual NRS
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the current GRADE approach, NRS studies for these types of questions begin with high certainty ratings. In particular, with prognostic studies, in which the issue is association and not causation, prognostic NRS begin as high certainty evidence.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Advantages and disadvantages of, in the context of GRADE, assessing risk of bias for individual studies using the ROBINS-I approach of specifying target trials > Advantages
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Another potential advantage of using an approach such as ROBINS-I is that it may harmonize GRADE approaches across different study types for different types of questions such as prognosis or test accuracy.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Advantages and disadvantages of, in the context of GRADE, assessing risk of bias for individual studies using the ROBINS-I approach of specifying target trials > Advantages
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, 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.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > ROBINS-I and GRADE
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“GRADE has thus accounted for issues that mitigate concerns about confounding and selection bias by introducing the upgrading domains. They will need to be considered in an assessment of a body of evidence when using ROBINS-I.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > What makes us confident in results of NRS and does GRADE already account for this? > Large effects and dose responses
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Currently GRADE has only three labels for risk of bias: not serious, serious or very serious on the risk of bias domain levels. For RCTs, this corresponds to a rating of the body of evidence as high, moderate or low certainty of evidence after considering risk of bias; for NRS this means that when GRADE currently uses serious risk of bias for NRS they are rated down from low initial certainty to”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Unresolved Issues
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For studies of interventions that are assessed with ROBINS-I in the context of GRADE, we suggest that an initial rating of high is used, with appropriate consideration of the impact of lack of randomization leading to rating down for risk of bias according to the ROBINS-I tool. In practice this will generally lead to rating down by at least two levels to low or very low certainty for NRS.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Summary and next steps
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: 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 paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ROBINS-I evaluates risk of bias in estimates of the effects (harm or benefit) of one or more interventions from studies that did not use randomization to allocate units (individuals or clusters of individuals) to comparison groups (in GRADE terminology observational studies).”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Rating risk of bias in individual observational studies
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, because of the advantage of assessing risk of bias on an absolute scale, use of ROBINS-I may facilitate combining results of RCTs and NRS. However, under what conditions one should combine results from randomized and non-randomized studies remains uncertain (this uncertainty also applies whether one uses ROBINS-I or other instruments for assessing risk of bias in NRS) (Figure 4).”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Advantages and disadvantages of, in the context of GRADE, assessing risk of bias for individual studies using the ROBINS-I approach of specifying target trials > Disadvantages
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“If the relevant studies are randomized trials, the body of evidence begins as high certainty. If the relevant studies are observational, the body of evidence begins as low certainty. This initial rating is followed by consideration of eight domains, five of which may result in rating down certainty, and three in rating up.(8)”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > GRADE’s approach to the certainty of the evidence from observational studies
What the sources say
- Not checked yetcites [8] Guyatt, 2013, GRADE guidelines: 11. Making an overall rating of confidence in effect estimates for a single outcome and for all outcomes, J Clin Epidemiol source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The evaluation of risk of bias across studies revealed that all residual plausible confounding – the major issues being that for-profit hospitals have on average higher income patients and greater resources - would have decreased the observed effect (further towards a RR of 1.0). Despite the biases in favour of for-profit hospitals, those hospitals demonstrated higher mortality – therefore the”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > What makes us confident in results of NRS and does GRADE already account for this? > All plausible residual confounders or other biases increase our certainty in the estimated effect
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Signaling questions in the ROBINS-I instrument ask respondents to rate RoB in domains of 1) Bias due to confounding, 2) Bias in selection of participants into the study, 3) Bias in classification of interventions, 4) Bias due to departures from intended interventions, 5) Bias due to missing data, 6) Bias in measurement of outcomes, and 7) Bias in selection of reported results (Figure 2).”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Rating risk of bias in individual observational studies
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, we would anticipate that whether one begins with a body of evidence from NRS studies as low certainty and looks for reasons to rate up or down, or starts with that evidence as high quality and looks for reasons to rate down, the final certainty rating should be the same.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Certainty of evidence for a body of evidence from NRS when using ROBINS-I for assessing risk of bias in individual NRS
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“First, ROBINS-I is currently the only available tool that explicitly includes a comparison against RCTs (and this situation is unlikely to change) and thus this guidance only applies to the situations when ROBINS-I is used.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Advantages and disadvantages of, in the context of GRADE, assessing risk of bias for individual studies using the ROBINS-I approach of specifying target trials > Disadvantages
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Risk of bias can be best mitigated by a well conducted RCT that balances known and unknown confounders, and using the Cochrane RoB 2.0 tool or similar assessment tools for RCTs to assess risk of bias.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Summary and next steps
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: A high-quality randomized trial avoids confounding and other biases such as selection or information biases.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, for results with large effects, or dose response, or results in which inference is strengthened by the plausible biases that exist, the extent of rating down may be lowered.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Summary and next steps
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ROBINS-I’s fundamental underlying principles are that (1) the study’s risk of bias is compared against a target RCT, even if this RCT may not be feasible or ethical; (2) the assessment of confounding and selection bias are integral parts of the tool; and (3) for a given result for a specific outcome, evidence from an NRS is assessed, addressing a number of domains and then giving an overall rating”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Rating risk of bias in individual observational studies
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Among other features, ROBINS-I allows review authors to assess how failure to use randomization in individual studies has impacted on risk of bias. For example, ROBINS-I allows a categorization of the magnitude of bias from lack of randomization through the selection and confounding bias domains, allows application of this assessment across risk of bias domains, and evaluation of how this differs”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Advantages and disadvantages of, in the context of GRADE, assessing risk of bias for individual studies using the ROBINS-I approach of specifying target trials > Advantages
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“available evidence suggests publication bias is a greater problem in NRS than in RCTs”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Unresolved Issues
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“An alternative approach is to determine risk of bias of observational studies in relation to the effect that would be seen in a high quality randomized trial. Such a trial avoids both confounding (through random allocation to interventions) as well as other sources of bias such as selection or information biases.”
§ GRADE Guidelines: 18. How ROBINS-I and other tools to assess risk of bias in non-randomized studies should be used to rate the certainty of a body of evidence > Rating risk of bias in individual observational studies
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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.”
Claims other papers make about this one
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The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A modified Cochrane Collaboration's Risk of Bias in Non-Randomized Studies of Interventions was used to evaluate the risk of bias in selected studies as low, moderate, serious, and critical based on 6 domains (Table 3) (29,30).”
From Alterations in Heart Rate Variability Associated With Irritable Bowel Syndrome or Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis (2021) · cites this paper as [30]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“the quantification of risk of bias (RoB) was determined via ROBINS-I tool [14].”
From Periodontal Health in Patients with Self-Ligating Brackets: A Systematic Review of Clinical Studies (2022) · cites this paper as [14]
Citations
Cites
Dermyshi, E., Wang, Y., Yan, C., Hong, W., Qiu, G., Gong, X., & Zhang, T. (2017) · Neonatology · doi:10.1159/000454668 · Cited by 24,586
ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions
Sterne, J. A., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., Viswanathan, M., Henry, D., Altman, D. G., Ansari, M. T., Boutron, I., et al. (2016) · BMJ · doi:10.1136/bmj.i4919 · Cited by 20,329
Self-citationGRADE guidelines: 1. Introduction—GRADE evidence profiles and summary of findings tables
Guyatt, G., Oxman, A. D., Akl, E. A., Kunz, R., Vist, G., Brozek, J., Norris, S., Falck-Ytter, Y., Glasziou, P., & deBeer, H. (2011) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.04.026 · Cited by 10,681
Self-citationGrading quality of evidence and strength of recommendations
(2004) · BMJ · doi:10.1136/bmj.328.7454.1490 · Cited by 8,477
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8. Herpes zoster and post herpetic neuralgia
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Comparison of Postoperative Outcomes Among Patients Treated by Male Versus Female Surgeons
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Convalescent plasma for people with COVID-19: a living systematic review
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The Route, Dose, and Interval of Epinephrine for Neonatal Resuscitation: A Systematic Review
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Vitamin C for COVID-19: A living systematic review
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Weaponised uranium and adverse health outcomes in Iraq: a systematic review
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Convalescent plasma for people with COVID-19: a living systematic review
Iannizzi, C., Chai, K. L., Piechotta, V., Valk, S. J., Kimber, C., Monsef, I., Wood, E. M., Lamikanra, A., Roberts, D. J., McQuilten, Z., et al. (2023) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013600.pub6 · Cited by 41
The effect of chronic low back pain on postural control during quiet standing: A meta-analysis
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Effectiveness of interprofessional tracheostomy teams: A systematic review
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Vitamins C, E, and β-Carotene and Risk of Type 2 Diabetes: A Systematic Review and Meta-Analysis
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Survival and Complications in Pediatric Patients With Cancer and COVID-19: A Meta-Analysis
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Evidence Evaluation Process and Management of Potential Conflicts of Interest
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“Quality appraisal A modified Cochrane Collaboration’s Risk of Bias in NonRandomized Studies of Interventionswas used to evaluate the risk of bias in selected studies as low, moderate, serious, and critical based on 6 domains (Table 3) (29,30).”
MethodReporting of systematic reviews and meta-analysis of observational studies
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Effectiveness of aortic valve replacement in Heyde syndrome: a meta-analysis
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Dietary Patterns and Cardiovascular Diseases in Asia: A Systematic Review and Meta-Analysis
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Interventions to Prevent Drugged Driving: A Systematic Review
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Walking speed and the risk of type 2 diabetes: a systematic review and meta-analysis
Jayedi, A., Zargar, M. S., Emadi, A., & Aune, D. (2023) · British Journal of Sports Medicine · doi:10.1136/bjsports-2023-107336 · Cited by 40
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Development of an Evidence-Based Risk Assessment Framework
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Rapid reviews methods series: Guidance on assessing the certainty of evidence
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Prenatal Cannabis Use and Neonatal Outcomes
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A Theoretical Framework and Competency-Based Approach to Training in Guideline Development
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Effect of periodontal therapy on COPD outcomes: a systematic review
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Increasing FDA-accelerated approval of single-arm trials in oncology (1992 to 2020)
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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.
- ORCID
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 111
- Pages
- 105-114
- ISSN
- 0895-4356 (ISSN-L)
- PMC
- PMC6692166
- OpenAlex
- W2792308249
- Semantic Scholar
- cf0d116e848fca50508ac07e082d05e29ba05fcd
- 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
- Open access
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-04, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-04 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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