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Reem A Mustafa,
Nancy Santesso, Jan Brozek,
Elie A Akl,
Stephen D Walter,
Geoff Norman,
Mahan Kulasegaram,
Robin Christensen,
Gordon H Guyatt,
Yngve Falck-Ytter, [......],
Immaculate F Nevis,
Stephen Gentles,
Marie-Chantal Ethier,
Alonso Carrasco-Labra,
Rasha Khatib,
Gihad Nesrallah,
Jamie Kroft,
Amanda Selk,
Romina Brignardello-Petersen,
Holger J Schünemann
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ABSTRACT: OBJECTIVE: We evaluated the inter-rater reliability (IRR) of assessing the quality of evidence (QoE) using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. STUDY DESIGN AND SETTING: On completing two training exercises, participants worked independently as individual raters to assess the QoE of 16 outcomes. After recording their initial impression using a global rating, raters graded the QoE following the GRADE approach. Subsequently, randomly paired raters submitted a consensus rating. RESULTS: The IRR without using the GRADE approach for two individual raters was 0.31 (95% confidence interval [95% CI] = 0.21-0.42) among Health Research Methodology students (n = 10) and 0.27 (95% CI = 0.19-0.37) among the GRADE working group members (n = 15). The corresponding IRR of the GRADE approach in assessing the QoE was significantly higher, that is, 0.66 (95% CI = 0.56-0.75) and 0.72 (95% CI = 0.61-0.79), respectively. The IRR further increased for three (0.80 [95% CI = 0.73-0.86] and 0.74 [95% CI = 0.65-0.81]) or four raters (0.84 [95% CI = 0.78-0.89] and 0.79 [95% CI = 0.71-0.85]). The IRR did not improve when QoE was assessed through a consensus rating. CONCLUSION: Our findings suggest that trained individuals using the GRADE approach improves reliability in comparison to intuitive judgments about the QoE and that two individual raters can reliably assess the QoE using the GRADE system.
Journal of clinical epidemiology 04/2013; · 2.96 Impact Factor
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Jeffrey C Andrews,
Holger J Schünemann,
Andrew D Oxman,
Kevin Pottie,
Joerg J Meerpohl,
Pablo Alonso Coello,
David Rind,
Victor Montori,
Juan Pablo Brito Campana,
Susan Norris,
Mahmoud Elbarbary,
Piet Post,
Mona Nasser,
Vijay Shukla,
Roman Jaeschke, Jan Brozek,
Ben Djulbegovic,
Gordon Guyatt
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ABSTRACT: In the GRADE approach, the strength of a recommendation reflects the extent to which we can be confident that the composite desirable effects of a management strategy outweigh the composite undesirable effects. This article addresses GRADE's approach to determining the direction and strength of a recommendation. The GRADE describes the balance of desirable and undesirable outcomes of interest among alternative management strategies depending on four domains, namely estimates of effect for desirable and undesirable outcomes of interest, confidence in the estimates of effect, estimates of values and preferences, and resource use. Ultimately, guideline panels must use judgment in integrating these factors to make a strong or weak recommendation for or against an intervention.
Journal of clinical epidemiology 04/2013; · 2.96 Impact Factor
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Jeff Andrews,
Gordon Guyatt,
Andrew D Oxman,
Phil Alderson,
Philipp Dahm,
Yngve Falck-Ytter,
Mona Nasser,
Joerg Meerpohl,
Piet N Post,
Regina Kunz, Jan Brozek,
Gunn Vist,
David Rind,
Elie A Akl,
Holger J Schünemann
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ABSTRACT: This article describes the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to classifying the direction and strength of recommendations. The strength of a recommendation, separated into strong and weak, is defined as the extent to which one can be confident that the desirable effects of an intervention outweigh its undesirable effects. Alternative terms for a weak recommendation include conditional, discretionary, or qualified. The strength of a recommendation has specific implications for patients, the public, clinicians, and policy makers. Occasionally, guideline developers may choose to make "only-in-research" recommendations. Although panels may choose not to make recommendations, this choice leaves those looking for answers from guidelines without the guidance they are seeking. GRADE therefore encourages panels to, wherever possible, offer recommendations.
Journal of clinical epidemiology 01/2013; · 2.96 Impact Factor
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Gordon H Guyatt,
Kristian Thorlund,
Andrew D Oxman,
Stephen D Walter,
Donald Patrick,
Toshi A Furukawa,
Bradley C Johnston,
Paul Karanicolas,
Elie A Akl,
Gunn Vist,
Regina Kunz, Jan Brozek,
Lawrence L Kupper,
Sandra L Martin,
Joerg J Meerpohl,
Pablo Alonso-Coello,
Robin Christensen,
Holger J Schunemann
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ABSTRACT: Presenting continuous outcomes in Summary of Findings tables presents particular challenges to interpretation. When each study uses the same outcome measure, and the units of that measure are intuitively interpretable (e.g., duration of hospitalization, duration of symptoms), presenting differences in means is usually desirable. When the natural units of the outcome measure are not easily interpretable, choosing a threshold to create a binary outcome and presenting relative and absolute effects become a more attractive alternative. When studies use different measures of the same construct, calculating summary measures requires converting to the same units of measurement for each study. The longest standing and most widely used approach is to divide the difference in means in each study by its standard deviation and present pooled results in standard deviation units (standardized mean difference). Disadvantages of this approach include vulnerability to varying degrees of heterogeneity in the underlying populations and difficulties in interpretation. Alternatives include presenting results in the units of the most popular or interpretable measure, converting to dichotomous measures and presenting relative and absolute effects, presenting the ratio of the means of intervention and control groups, and presenting the results in minimally important difference units. We outline the merits and limitations of each alternative and provide guidance for meta-analysts and guideline developers.
Journal of clinical epidemiology 10/2012; · 2.96 Impact Factor
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Annals of internal medicine 09/2012; 157(5):386-387. · 16.73 Impact Factor
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ABSTRACT: To describe the impact of the diagnosis and rationale for action against cow's milk allergy (DRACMA) guidelines on the decision process in the therapy of cow's milk allergy (CMA).
We report here the experience of a 2-year application of DRACMA worldwide. Variations in the socioeconomic profile of CMA sufferers and their context can modify the application of DRACMA recommendations. As an example, we use the country-by-country modifications of the social structure and the modifications of the prices for special formula in Italy.
The DRACMA guidelines were issued to inform formula choice for CMA treatment by integrating patients' underlying values, preferences and remarks into grading of recommendations assessment, development and evaluation (GRADE) recommendations, which serve to facilitate their interpretation. This method allows every pediatrician/allergist to follow the changing variables of formulas (cost, palatability, nutritional value) and tailor their prescription for individual patients accordingly. The art of CMA treatment has always relied on physicians' interpretation and the goal of the DRACMA guidelines is to provide a rationale-based and evidence-based indication for choosing an appropriate formula.
Current Opinion in Allergy and Clinical Immunology 06/2012; 12(3):316-22. · 4.11 Impact Factor
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Gordon H Guyatt,
Andrew D Oxman,
Nancy Santesso,
Mark Helfand,
Gunn Vist,
Regina Kunz, Jan Brozek,
Susan Norris,
Joerg Meerpohl,
Ben Djulbecovic,
Pablo Alonso-Coello,
Piet N Post,
Jason W Busse,
Paul Glasziou,
Robin Christensen,
Holger J Schünemann
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ABSTRACT: Summary of Findings (SoF) tables present, for each of the seven (or fewer) most important outcomes, the following: the number of studies and number of participants; the confidence in effect estimates (quality of evidence); and the best estimates of relative and absolute effects. Potentially challenging choices in preparing SoF table include using direct evidence (which may have very few events) or indirect evidence (from a surrogate) as the best evidence for a treatment effect. If a surrogate is chosen, it must be labeled as substituting for the corresponding patient-important outcome. Another such choice is presenting evidence from low-quality randomized trials or high-quality observational studies. When in doubt, a reasonable approach is to present both sets of evidence; if the two bodies of evidence have similar quality but discrepant results, one would rate down further for inconsistency. For binary outcomes, relative risks (RRs) are the preferred measure of relative effect and, in most instances, are applied to the baseline or control group risks to generate absolute risks. Ideally, the baseline risks come from observational studies including representative patients and identifying easily measured prognostic factors that define groups at differing risk. In the absence of such studies, relevant randomized trials provide estimates of baseline risk. When confidence intervals (CIs) around the relative effect include no difference, one may simply state in the absolute risk column that results fail to show a difference, omit the point estimate and report only the CIs, or add a comment emphasizing the uncertainty associated with the point estimate.
Journal of clinical epidemiology 05/2012; · 2.96 Impact Factor
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Gordon Guyatt,
Andrew D Oxman,
Shahnaz Sultan, Jan Brozek,
Paul Glasziou,
Pablo Alonso-Coello,
David Atkins,
Regina Kunz,
Victor Montori,
Roman Jaeschke,
David Rind,
Philipp Dahm,
Elie A Akl,
Joerg Meerpohl,
Gunn Vist,
Elise Berliner,
Susan Norris,
Yngve Falck-Ytter,
Holger J Schünemann
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ABSTRACT: GRADE requires guideline developers to make an overall rating of confidence in estimates of effect (quality of evidence-high, moderate, low, or very low) for each important or critical outcome. GRADE suggests, for each outcome, the initial separate consideration of five domains of reasons for rating down the confidence in effect estimates, thereby allowing systematic review authors and guideline developers to arrive at an outcome-specific rating of confidence. Although this rating system represents discrete steps on an ordinal scale, it is helpful to view confidence in estimates as a continuum, and the final rating of confidence may differ from that suggested by separate consideration of each domain. An overall rating of confidence in estimates of effect is only relevant in settings when recommendations are being made. In general, it is based on the critical outcome that provides the lowest confidence.
Journal of clinical epidemiology 04/2012; · 2.96 Impact Factor
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Gordon H Guyatt,
Andrew D Oxman,
Regina Kunz, Jan Brozek,
Pablo Alonso-Coello,
David Rind,
P J Devereaux,
Victor M Montori,
Bo Freyschuss,
Gunn Vist,
Roman Jaeschke,
John W Williams,
Mohammad Hassan Murad,
David Sinclair,
Yngve Falck-Ytter,
Joerg Meerpohl,
Craig Whittington,
Kristian Thorlund,
Jeff Andrews,
Holger J Schünemann
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ABSTRACT: GRADE suggests that examination of 95% confidence intervals (CIs) provides the optimal primary approach to decisions regarding imprecision. For practice guidelines, rating down the quality of evidence (i.e., confidence in estimates of effect) is required if clinical action would differ if the upper versus the lower boundary of the CI represented the truth. An exception to this rule occurs when an effect is large, and consideration of CIs alone suggests a robust effect, but the total sample size is not large and the number of events is small. Under these circumstances, one should consider rating down for imprecision. To inform this decision, one can calculate the number of patients required for an adequately powered individual trial (termed the "optimal information size" [OIS]). For continuous variables, we suggest a similar process, initially considering the upper and lower limits of the CI, and subsequently calculating an OIS. Systematic reviews require a somewhat different approach. If the 95% CI excludes a relative risk (RR) of 1.0, and the total number of events or patients exceeds the OIS criterion, precision is adequate. If the 95% CI includes appreciable benefit or harm (we suggest an RR of under 0.75 or over 1.25 as a rough guide) rating down for imprecision may be appropriate even if OIS criteria are met.
Journal of clinical epidemiology 08/2011; 64(12):1283-93. · 2.96 Impact Factor
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Gordon H Guyatt,
Andrew D Oxman,
Regina Kunz,
James Woodcock, Jan Brozek,
Mark Helfand,
Pablo Alonso-Coello,
Paul Glasziou,
Roman Jaeschke,
Elie A Akl,
Susan Norris,
Gunn Vist,
Philipp Dahm,
Vijay K Shukla,
Julian Higgins,
Yngve Falck-Ytter,
Holger J Schünemann
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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.
Journal of clinical epidemiology 07/2011; 64(12):1294-302. · 2.96 Impact Factor
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Gordon H Guyatt,
Andrew D Oxman,
Regina Kunz,
James Woodcock, Jan Brozek,
Mark Helfand,
Pablo Alonso-Coello,
Yngve Falck-Ytter,
Roman Jaeschke,
Gunn Vist,
Elie A Akl,
Piet N Post,
Susan Norris,
Joerg Meerpohl,
Vijay K Shukla,
Mona Nasser,
Holger J Schünemann
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ABSTRACT: Direct evidence comes from research that directly compares the interventions in which we are interested when applied to the populations in which we are interested and measures outcomes important to patients. Evidence can be indirect in one of four ways. First, patients may differ from those of interest (the term applicability is often used for this form of indirectness). Secondly, the intervention tested may differ from the intervention of interest. Decisions regarding indirectness of patients and interventions depend on an understanding of whether biological or social factors are sufficiently different that one might expect substantial differences in the magnitude of effect. Thirdly, outcomes may differ from those of primary interest-for instance, surrogate outcomes that are not themselves important, but measured in the presumption that changes in the surrogate reflect changes in an outcome important to patients. A fourth type of indirectness, conceptually different from the first three, occurs when clinicians must choose between interventions that have not been tested in head-to-head comparisons. Making comparisons between treatments under these circumstances requires specific statistical methods and will be rated down in quality one or two levels depending on the extent of differences between the patient populations, co-interventions, measurements of the outcome, and the methods of the trials of the candidate interventions.
Journal of clinical epidemiology 07/2011; 64(12):1303-10. · 2.96 Impact Factor
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Gordon H Guyatt,
Andrew D Oxman,
Shahnaz Sultan,
Paul Glasziou,
Elie A Akl,
Pablo Alonso-Coello,
David Atkins,
Regina Kunz, Jan Brozek,
Victor Montori,
Roman Jaeschke,
David Rind,
Philipp Dahm,
Joerg Meerpohl,
Gunn Vist,
Elise Berliner,
Susan Norris,
Yngve Falck-Ytter,
M Hassan Murad,
Holger J Schünemann
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ABSTRACT: The most common reason for rating up the quality of evidence is a large effect. GRADE suggests considering rating up quality of evidence one level when methodologically rigorous observational studies show at least a two-fold reduction or increase in risk, and rating up two levels for at least a five-fold reduction or increase in risk. Systematic review authors and guideline developers may also consider rating up quality of evidence when a dose-response gradient is present, and when all plausible confounders or biases would decrease an apparent treatment effect, or would create a spurious effect when results suggest no effect. Other considerations include the rapidity of the response, the underlying trajectory of the condition, and indirect evidence.
Journal of clinical epidemiology 07/2011; 64(12):1311-6. · 2.96 Impact Factor
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Gordon H Guyatt,
Andrew D Oxman,
Victor Montori,
Gunn Vist,
Regina Kunz, Jan Brozek,
Pablo Alonso-Coello,
Ben Djulbegovic,
David Atkins,
Yngve Falck-Ytter,
John W Williams,
Joerg Meerpohl,
Susan L Norris,
Elie A Akl,
Holger J Schünemann
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ABSTRACT: In the GRADE approach, randomized trials start as high-quality evidence and observational studies as low-quality evidence, but both can be rated down if a body of evidence is associated with a high risk of publication bias. Even when individual studies included in best-evidence summaries have a low risk of bias, publication bias can result in substantial overestimates of effect. Authors should suspect publication bias when available evidence comes from a number of small studies, most of which have been commercially funded. A number of approaches based on examination of the pattern of data are available to help assess publication bias. The most popular of these is the funnel plot; all, however, have substantial limitations. Publication bias is likely frequent, and caution in the face of early results, particularly with small sample size and number of events, is warranted.
Journal of clinical epidemiology 07/2011; 64(12):1277-82. · 2.96 Impact Factor
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Howard Balshem,
Mark Helfand,
Holger J Schünemann,
Andrew D Oxman,
Regina Kunz, Jan Brozek,
Gunn E Vist,
Yngve Falck-Ytter,
Joerg Meerpohl,
Susan Norris,
Gordon H Guyatt
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ABSTRACT: 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.
Journal of clinical epidemiology 04/2011; 64(4):401-6. · 2.96 Impact Factor
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ABSTRACT: 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.
Journal of clinical epidemiology 04/2011; 64(4):395-400. · 2.96 Impact Factor
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Gordon Guyatt,
Andrew D Oxman,
Elie A Akl,
Regina Kunz,
Gunn Vist, Jan Brozek,
Susan Norris,
Yngve Falck-Ytter,
Paul Glasziou,
Hans DeBeer,
Roman Jaeschke,
David Rind,
Joerg Meerpohl,
Philipp Dahm,
Holger J Schünemann
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ABSTRACT: This article is the first of a series providing guidance for use of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system of rating quality of evidence and grading strength of recommendations in systematic reviews, health technology assessments (HTAs), and clinical practice guidelines addressing alternative management options. The GRADE process begins with asking an explicit question, including specification of all important outcomes. After the evidence is collected and summarized, GRADE provides explicit criteria for rating the quality of evidence that include study design, risk of bias, imprecision, inconsistency, indirectness, and magnitude of effect. Recommendations are characterized as strong or weak (alternative terms conditional or discretionary) according to the quality of the supporting evidence and the balance between desirable and undesirable consequences of the alternative management options. GRADE suggests summarizing evidence in succinct, transparent, and informative summary of findings tables that show the quality of evidence and the magnitude of relative and absolute effects for each important outcome and/or as evidence profiles that provide, in addition, detailed information about the reason for the quality of evidence rating. Subsequent articles in this series will address GRADE's approach to formulating questions, assessing quality of evidence, and developing recommendations.
Journal of clinical epidemiology 04/2011; 64(4):383-94. · 2.96 Impact Factor
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Gordon H Guyatt,
Andrew D Oxman,
Gunn Vist,
Regina Kunz, Jan Brozek,
Pablo Alonso-Coello,
Victor Montori,
Elie A Akl,
Ben Djulbegovic,
Yngve Falck-Ytter,
Susan L Norris,
John W Williams,
David Atkins,
Joerg Meerpohl,
Holger J Schünemann
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ABSTRACT: In the GRADE approach, randomized trials start as high-quality evidence and observational studies as low-quality evidence, but both can be rated down if most of the relevant evidence comes from studies that suffer from a high risk of bias. Well-established limitations of randomized trials include failure to conceal allocation, failure to blind, loss to follow-up, and failure to appropriately consider the intention-to-treat principle. More recently recognized limitations include stopping early for apparent benefit and selective reporting of outcomes according to the results. Key limitations of observational studies include use of inappropriate controls and failure to adequately adjust for prognostic imbalance. Risk of bias may vary across outcomes (e.g., loss to follow-up may be far less for all-cause mortality than for quality of life), a consideration that many systematic reviews ignore. In deciding whether to rate down for risk of bias--whether for randomized trials or observational studies--authors should not take an approach that averages across studies. Rather, for any individual outcome, when there are some studies with a high risk, and some with a low risk of bias, they should consider including only the studies with a lower risk of bias.
Journal of clinical epidemiology 04/2011; 64(4):407-15. · 2.96 Impact Factor
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Philippe J Bousquet,
Moisés A Calderón,
Pascal Demoly,
Désirée Larenas,
Giovanni Passalacqua,
Claus Bachert, Jan Brozek,
G Walter Canonica,
Thomas Casale,
Joao Fonseca,
Ronald Dahl,
Stephen R Durham,
Hans Merk,
Margitta Worm,
Ulrich Wahn,
Torsten Zuberbier,
Holger J Schünemann,
Jean Bousquet
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ABSTRACT: Randomized trials provide evidence to inform treatment decisions. The Consolidated Standards of Reporting Trials (CONSORT) Statement is a set of recommendations for the reporting of trials.
We sought to assess the quality of reporting allergen-specific immunotherapy trials according to CONSORT criteria.
The reporting of the procedure, randomization, dropouts, strict conduct of intention-to-treat (ITT) analysis, and sample size calculation according to CONSORT were assessed in the 46 subcutaneous and 48 sublingual immunotherapy (SLIT) blind, placebo-controlled randomized trials published between 1996 and 2009 in English.
One subcutaneous immunotherapy (2.2%) and 3 SLIT (6.6%) trials met CONSORT Statement criteria. These were used for the registration of sublingual tablets to the European Medicines Agency. In subcutaneous immunotherapy, 16 (35%) studies reported a CONSORT flow chart, and 12 (26%) provided a description of dropouts. Adequate randomization was reported in 9 (35%) studies, and incomplete randomization was reported in 15 (33%). Power analysis was reported in 15 (33%) studies. In SLIT, 20 (42%) studies reported a CONSORT flow chart, and 16 (32%) a description of dropouts. ITT analysis was carried out in 1 (2.2%) SLIT study, and a modified ITT analysis was used in 1 (2.2%) subcutaneous immunotherapy study and 2 (4.4%) SLIT studies. Adequate randomization was reported in 6 (12%) studies, and incomplete randomization was reported in 16 (32%). Power analysis was reported in 15 (27%) studies.
As in other areas of medicine, the quality of reporting of most immunotherapy trials is low, and only 4.2% of SLIT randomized controlled trials met all of the criteria of the CONSORT Statement. Use of the CONSORT criteria should be encouraged.
The Journal of allergy and clinical immunology 01/2011; 127(1):49-56, 56.e1-11. · 9.17 Impact Factor
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Alessandro Fiocchi,
Holger J Schünemann, Jan Brozek,
Patrizia Restani,
Kirsten Beyer,
Riccardo Troncone,
Alberto Martelli,
Luigi Terracciano,
Sami L Bahna,
Fabienne Rancé,
Motohiro Ebisawa,
Ralf G Heine,
Amal Assa'ad,
Hugh Sampson,
Elvira Verduci,
G R Bouygue,
Carlos Baena-Cagnani,
Walter Canonica,
Richard F Lockey
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ABSTRACT: The 2nd Milan Meeting on Adverse Reactions to Bovine Proteins was the venue for the presentation of the first consensus-based approach to the management of cow's milk allergy. It was also the first time that the Grading of Recommendations, Assessments, Development, and Evaluation approach for formulating guidelines and recommendations was applied to the field of food allergy. In this report we present the contributions in allergen science, epidemiology, natural history, evidence-based diagnosis, and therapy synthesized in the World Allergy Organization Diagnosis and Rationale for Action against Cow's Milk Allergy guidelines and presented during the meeting. A consensus emerged between discussants that cow's milk allergy management should reflect not only basic research but also a newer and better appraisal of the literature in the light of the values and preferences shared by patients and their caregivers in partnership. In the field of diagnosis, atopy patch testing and microarray technology have not yet evolved for use outside the research setting. With foreseeable breakthroughs (eg, immunotherapy and molecular diagnosis) in the offing, the step ahead in leadership can only stem from a worldwide organization implementing consensus-based clinical practice guidelines to diffuse and share clinical knowledge.
The Journal of allergy and clinical immunology 12/2010; 126(6):1119-28.e12. · 9.17 Impact Factor
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ABSTRACT: An exposition of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to recommendations.
In this review, we outline the process whereby the strength of evidence from the literature undergoes a systematic reappraisal. The GRADE system allows four grades of evidence (high quality, moderate, low, and very low) and strength of recommendation is qualified as strong, weak, or conditional to an intervention (pro or con) and defined as the level of confidence that desirable effects predominate over untoward ones with a certain intervention. We provide research and clinical reviews in various settings in which this approach has been used.
Evidence-based medicine requires integrating the best available 'benchmark' literature with patient preferences and values (bedside) and is an evaluation process involving both patient and clinician, with a systematic assessment of the rated evidence from state-of-the-art medical literature. The GRADE methodology was developed as an application of evidence-based medicine to the field of recommendations and their formulation. The GRADE working group brings together clinical researchers and methodologists who developed a rating system to assess the quality of evidence for the purpose of making clinical practice recommendations.
Current Opinion in Allergy and Clinical Immunology 08/2010; 10(4):377-83. · 4.11 Impact Factor
