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      GRADE guidelines: 7. Rating the quality of evidence—inconsistency

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          Abstract

          This article deals with inconsistency of relative (rather than absolute) treatment effects in binary/dichotomous outcomes. A body of evidence is not rated up in quality if studies yield consistent results, but may be rated down in quality if inconsistent. Criteria for evaluating consistency include similarity of point estimates, extent of overlap of confidence intervals, and statistical criteria including tests of heterogeneity and I(2). To explore heterogeneity, systematic review authors should generate and test a small number of a priori hypotheses related to patients, interventions, outcomes, and methodology. When inconsistency is large and unexplained, rating down quality for inconsistency is appropriate, particularly if some studies suggest substantial benefit, and others no effect or harm (rather than only large vs. small effects). Apparent subgroup effects may be spurious. Credibility is increased if subgroup effects are based on a small number of a priori hypotheses with a specified direction; subgroup comparisons come from within rather than between studies; tests of interaction generate low P-values; and have a biological rationale. Copyright © 2011 Elsevier Inc. All rights reserved.

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          GRADE guidelines: 3. Rating the quality of evidence.

          This article introduces the approach of GRADE to rating quality of evidence. GRADE specifies four categories-high, moderate, low, and very low-that are applied to a body of evidence, not to individual studies. In the context of a systematic review, quality reflects our confidence that the estimates of the effect are correct. In the context of recommendations, quality reflects our confidence that the effect estimates are adequate to support a particular recommendation. Randomized trials begin as high-quality evidence, observational studies as low quality. "Quality" as used in GRADE means more than risk of bias and so may also be compromised by imprecision, inconsistency, indirectness of study results, and publication bias. In addition, several factors can increase our confidence in an estimate of effect. GRADE provides a systematic approach for considering and reporting each of these factors. GRADE separates the process of assessing quality of evidence from the process of making recommendations. Judgments about the strength of a recommendation depend on more than just the quality of evidence. Copyright © 2011 Elsevier Inc. All rights reserved.
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            GRADE guidelines: 2. Framing the question and deciding on important outcomes.

            GRADE requires a clear specification of the relevant setting, population, intervention, and comparator. It also requires specification of all important outcomes--whether evidence from research studies is, or is not, available. For a particular management question, the population, intervention, and outcome should be sufficiently similar across studies that a similar magnitude of effect is plausible. Guideline developers should specify the relative importance of the outcomes before gathering the evidence and again when evidence summaries are complete. In considering the importance of a surrogate outcome, authors should rate the importance of the patient-important outcome for which the surrogate is a substitute and subsequently rate down the quality of evidence for indirectness of outcome. Copyright © 2011 Elsevier Inc. All rights reserved.
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              Is Open Access

              Undue reliance on I2 in assessing heterogeneity may mislead

              Background The heterogeneity statistic I 2, interpreted as the percentage of variability due to heterogeneity between studies rather than sampling error, depends on precision, that is, the size of the studies included. Methods Based on a real meta-analysis, we simulate artificially 'inflating' the sample size under the random effects model. For a given inflation factor M = 1, 2, 3,... and for each trial i, we create a M-inflated trial by drawing a treatment effect estimate from the random effects model, using s i 2 /M as within-trial sampling variance. Results As precision increases, while estimates of the heterogeneity variance τ 2 remain unchanged on average, estimates of I 2 increase rapidly to nearly 100%. A similar phenomenon is apparent in a sample of 157 meta-analyses. Conclusion When deciding whether or not to pool treatment estimates in a meta-analysis, the yard-stick should be the clinical relevance of any heterogeneity present. τ 2, rather than I 2, is the appropriate measure for this purpose.
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                Author and article information

                Journal
                Journal of Clinical Epidemiology
                Journal of Clinical Epidemiology
                Elsevier BV
                08954356
                December 2011
                December 2011
                : 64
                : 12
                : 1294-1302
                Article
                10.1016/j.jclinepi.2011.03.017
                21803546
                aa4a2623-e82f-4d1e-bf3f-f1502c318d06
                © 2011

                https://www.elsevier.com/tdm/userlicense/1.0/

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