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To provide practical principles and examples to help GRADE users make optimal choices regarding their ratings of certainty of evidence using a minimally or partially contextualized approach. Based on the GRADE clarification of certainty of evidence in 2017, a project group within the GRADE Working Group conducted iterative discussions and presentations at GRADE Working Group meetings to refine this construct and produce practical guidance. Systematic review and health technology assessment authors need to clarify what it is in which they are rating their certainty of evidence (i.e., the target of their certainty rating). The decision depends on the degree of contextualization (partially or minimally contextualized), thresholds (null, small, moderate or large effect threshold), and where the point estimate lies in relation to the chosen threshold(s). When the 95% confidence interval crosses multiple possible thresholds (i.e., including both large benefit and large harm), it is not worthwhile for authors to determine the target of certainty rating. GRADE provides practical principles to help systematic review and health technology assessment authors specify the target of their certainty of evidence rating.

This article provides GRADE guidance on how authors of evidence syntheses and health decision makers, including guideline developers, can rate the certainty across a body of evidence for comparative test accuracy questions. This guidance extends the previously published GRADE guidance for assessing certainty of evidence for test accuracy to scenarios in which two or more index tests are compared. Through an iterative brainstorm-discussion-feedback process within the GRADE working group, we developed a guidance accompanied by practical examples. Rating the certainty of evidence for comparative test accuracy shares many concepts and ideas with the existing GRADE guidance for test accuracy. The rating in comparisons of test accuracy requires additional considerations, such as the selection of appropriate comparative study designs, additional criteria for judging risk of bias, and the consequences of using comparative measures of test accuracy. Distinct approaches to rating certainty are required for comparative test accuracy studies and between-study (indirect) comparisons. This GRADE guidance will support transparent assessment of the certainty for a body of comparative test accuracy evidence.

To clarify the grading of recommendations assessment, development and evaluation (GRADE) definition of certainty of evidence and suggest possible approaches to rating certainty of the evidence for systematic reviews, health technology assessments, and guidelines. This work was carried out by a project group within the GRADE Working Group, through brainstorming and iterative refinement of ideas, using input from workshops, presentations, and discussions at GRADE Working Group meetings to produce this document, which constitutes official GRADE guidance. Certainty of evidence is best considered as the certainty that a true effect lies on one side of a specified threshold or within a chosen range. We define possible approaches for choosing threshold or range. For guidelines, what we call a fully contextualized approach requires simultaneously considering all critical outcomes and their relative value. Less-contextualized approaches, more appropriate for systematic reviews and health technology assessments, include using specified ranges of magnitude of effect, for example, ranges of what we might consider no effect, trivial, small, moderate, or large effects. It is desirable for systematic review authors, guideline panelists, and health technology assessors to specify the threshold or ranges they are using when rating the certainty in evidence.

This article describes conceptual advances of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) working group guidance to evaluate the certainty of evidence (confidence in evidence, quality of evidence) from network meta-analysis (NMA). Application of the original GRADE guidance, published in 2014, in a number of NMAs has resulted in advances that strengthen its conceptual basis and make the process more efficient. This guidance will be useful for systematic review authors who aim to assess the certainty of all pairwise comparisons from an NMA and who are familiar with the basic concepts of NMA and the traditional GRADE approach for pairwise meta-analysis. Two principles of the original GRADE NMA guidance are that we need to rate the certainty of the evidence for each pairwise comparison within a network separately and that in doing so we need to consider both the direct and indirect evidence. We present, discuss, and illustrate four conceptual advances: (1) consideration of imprecision is not necessary when rating the direct and indirect estimates to inform the rating of NMA estimates, (2) there is no need to rate the indirect evidence when the certainty of the direct evidence is high and the contribution of the direct evidence to the network estimate is at least as great as that of the indirect evidence, (3) we should not trust a statistical test of global incoherence of the network to assess incoherence at the pairwise comparison level, and (4) in the presence of incoherence between direct and indirect evidence, the certainty of the evidence of each estimate can help decide which estimate to believe. •The application of the Grading of Recommendations Assessments, Development, and Evaluation approach to a number of network meta-analyses in the 3 years since the original guidance publication has led to advances that have strengthened the conceptual basis.•We present, discuss, and illustrate four conceptual advances. These are based on two principles: we need to rate the certainty of the evidence of each pairwise comparison within a network separately and that we need to consider both the direct and indirect evidence contributing to each network estimate.•Although maximizing the efficiency of the process is desirable, as illustrated in the conceptual advances, use of these strategies requires careful judgment.

•Randomized controlled trials (RCTs) provide the best source of evidence for research syntheses estimating relative effects of an intervention.•Non-randomized studies of representative populations can provide the best evidence with respect to prognosis, baseline risk, test accuracy, and estimates of utility and values and preferences of different outcomes.•For many research questions randomized trials will be scarce or unavailable, and decision-makers might need to consider using non-randomized (observational) studies that can provide evidence about the effectiveness of interventions as replacement (in the absence of appropriate RCT evidence), sequential, or complementary to RCT evidence•GRADE guidance can help authors that are considering the inclusion of non-randomized studies in addition to RCTs during the evidence synthesis process. This is the 24th in the ongoing series of articles describing the GRADE approach for assessing the certainty of a body of evidence in systematic reviews and health technology assessments and how to move from evidence to recommendations in guidelines. Guideline developers and authors of systematic reviews and other evidence syntheses use randomized controlled studies (RCTs) and non-randomized studies of interventions (NRSI) as sources of evidence for questions about health interventions. RCTs with low risk of bias are the most trustworthy source of evidence for estimating relative effects of interventions because of protection against confounding and other biases. However, in several instances, NRSI can still provide valuable information as complementary, sequential, or replacement evidence for RCTs. In this article we offer guidance on the decision regarding when to search for and include either or both types of studies in systematic reviews to inform health recommendations. This work aims to help methodologists in review teams, technology assessors, guideline panelists, and anyone conducting evidence syntheses using GRADE.

This article provides updated GRADE guidance about how authors of systematic reviews and health technology assessments and guideline developers can rate the certainty of evidence (also known as quality of the evidence or confidence in the estimates) of a body of evidence addressing test accuracy (TA) on the domains imprecision, inconsistency, publication bias, and other domains. It also provides guidance for how to present synthesized information in evidence profiles and summary of findings tables. We present guidance for rating certainty in TA in clinical and public health and review the presentation of results of a body of evidence regarding tests. Supplemented by practical examples, we describe how raters of the evidence can apply the GRADE domains inconsistency, imprecision, and publication bias to a body of evidence of TA studies. Using GRADE in Cochrane and other reviews as well as World Health Organization and other guidelines helped refining the GRADE approach for rating the certainty of a body of evidence from TA studies. Although several of the GRADE domains (e.g., imprecision and magnitude of the association) require further methodological research to help operationalize them, judgments need to be made on the basis of what is known so far.

This article provides updated GRADE guidance about how authors of systematic reviews and health technology assessments and guideline developers can assess the results and the certainty of evidence (also known as quality of the evidence or confidence in the estimates) of a body of evidence addressing test accuracy (TA). We present an overview of the GRADE approach and guidance for rating certainty in TA in clinical and public health and review the presentation of results of a body of evidence regarding tests. Part 1 of the two parts in this 21st guidance article about how to apply GRADE focuses on understanding study design issues in test accuracy, provide an overview of the domains, and describe risk of bias and indirectness specifically. Supplemented by practical examples, we describe how raters of the evidence using GRADE can evaluate study designs focusing on tests and how they apply the GRADE domains risk of bias and indirectness to a body of evidence of TA studies. Rating the certainty of a body of evidence using GRADE in Cochrane and other reviews and World Health Organization and other guidelines dealing with in TA studies helped refining our approach. The resulting guidance will help applying GRADE successfully for questions and recommendations focusing on tests.

PurposeThe aim of current systematic review was to update the body of evidence on associations between adherence to the Mediterranean diet (MedDiet) and risk of cancer mortality, site-specific cancer in the general population; all-cause, and cancer mortality as well as cancer reoccurrence among cancer survivors.MethodsA literature search for randomized controlled trials (RCTs), case–control and cohort studies published up to April 2020 was performed using PubMed and Scopus. Study-specific risk estimates for the highest versus lowest adherence to the MedDiet category were pooled using random-effects meta-analyses. Certainty of evidence from cohort studies and RCTs was evaluated using the NutriGrade scoring system.ResultsThe updated search revealed 44 studies not identified in the previous review. Altogether, 117 studies including 3,202,496 participants were enclosed for meta-analysis. The highest adherence to MedDiet was inversely associated with cancer mortality (RRcohort: 0.87, 95% CI 0.82, 0.92; N = 18 studies), all-cause mortality among cancer survivors (RRcohort: 0.75, 95% CI 0.66, 0.86; N = 8), breast (RRobservational: 0.94, 95% CI 0.90, 0.97; N = 23), colorectal (RRobservational: 0.83, 95% CI 0.76, 0.90; N = 17), head and neck (RRobservational: 0.56, 95% CI 0.44, 0.72; N = 9), respiratory (RRcohort: 0.84, 95% CI 0.76, 0.94; N = 5), gastric (RRobservational: 0.70, 95% CI 0.61, 0.80; N = 7), bladder (RRobservational: 0.87, 95% CI 0.76, 0.98; N = 4), and liver cancer (RRobservational: 0.64, 95% CI 0.54, 0.75; N = 4). Adhering to MedDiet did not modify risk of blood, esophageal, pancreatic and prostate cancer risk.ConclusionIn conclusion, our results suggest that highest adherence to the MedDiet was related to lower risk of cancer mortality in the general population, and all-cause mortality among cancer survivors as well as colorectal, head and neck, respiratory, gastric, liver and bladder cancer risks. Moderate certainty of evidence from cohort studies suggest an inverse association for cancer mortality and colorectal cancer, but most of the comparisons were rated as low or very low certainty of evidence.

The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs). Expert consultations and an international multidisciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modeling community. Feedback from experts in a broad range of modeling and health care disciplines addressed the content validity of the approach. Workshop participants agreed that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose–response relation, and the direction of residual confounding) also apply when assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo, a model specific to the situation of interest, 2) identifying an existing model, the outputs of which provide the highest certainty evidence for the situation of interest, either “off-the-shelf” or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modeling and health care disciplines. This conceptual GRADE approach provides a framework for using evidence from models in health decision-making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modeling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care–related disciplines (e.g., therapeutic decision-making, toxicology, environmental health, and health economics).

The aim of the study was to develop a Grading of Recommendations, Assessment, Development and Evaluation (GRADE) summary of findings (SoF) table format that displays the critical information from a network meta-analysis (NMA). We applied a user experience model for data analysis based on four rounds of semistructured interviews. We interviewed 32 stakeholders who conduct or use MA. Four rounds of interviews produced six candidate NMA-SoF tables. Users found a final NMA-SoF table that included the following components highly acceptable: (1) details of the clinical question (PICO), (2) a plot depicting network geometry, (3) relative and absolute effect estimates, (4) certainty of evidence, (5) ranking of treatments, and (6) interpretation of findings. Using stakeholder feedback, we developed a new GRADE NMA-SoF table that includes the relevant components that facilitate understanding NMA findings and health decision-making.