Using systematic reviews in guideline development: The GRADE approach

Zhang, Yuan; Akl, Elie A.; Schünemann, Holger J.

doi:10.1002/jrsm.1313

Cited by 137 publications

(116 citation statements)

References 68 publications

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“…This dimension is based on the evaluation of the robustness of the clinical evidence submitted by manufacturer to support the request for drug innovation. For this purpose, AIFA decided to adopt the approach of the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system, a methodology already used by many international organizations to provide support in grading the quality (or certainty) of evidence for systematic reviews (i.e. Cochrane Collaboration) and the strength of recommendations for guidelines development.…”

Section: Methodsmentioning

confidence: 99%

Using GRADE methodology to assess innovation of new medicinal products in Italy

Fortinguerra

Tafuri

Trotta

et al. 2019

Brit J Clinical Pharma

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Aim: In April 2017 the Italian Medicine Agency (AIFA) developed new criteria to grant any new medicinal product with an innovative designation. The aim of this study is to describe this new model and how it works. Methods: A retrospective descriptive analysis was performed on the results of the assessment process of innovativeness of new medicinal products (or therapeutic indications) based on the AIFA's new innovation criteria (therapeutic need, added therapeutic value and quality of clinical evidence through GRADE methodology) carried out between April 2017 and February 2019 and made publicly available on the AIFA website starting from January 2018. Results: A total of 37 full reports (22 for oncological indications) explaining the rationale for the AIFA's decision is made publicly available on the agency's website. A total of 12 therapeutic indications (5 oncological) were evaluated as fully innovative, 13 indications (11 oncological) were evaluated as conditionally innovative, while 12 indications (6 oncological) as non-innovative. Conclusion:The new AIFA innovation criteria resulted in a much more flexible and transparent model to define and assess what constitutes a therapeutic innovation. In particular, the choice of AIFA to use the GRADE methodology to evaluate the quality of clinical evidence within a process of drug innovativeness assessment is essential for the early identification of the discrepancy between the need for patients of a rapid access to innovative therapies and the available clinical data needed to make decisions on drug innovativeness.

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Section: Methodsmentioning

confidence: 99%

Using GRADE methodology to assess innovation of new medicinal products in Italy

Fortinguerra

Tafuri

Trotta

et al. 2019

Brit J Clinical Pharma

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“…The parameter 2 is a nuisance parameter in the random-effects model that has no intuitive clinical interpretation as opposed to log OR. Nevertheless, is an important parameter in the context of inconsistency when we evaluate the certainty of the evidence from a pairwise or network meta-analysis using the GRADE framework (36,37). The magnitude of 2 affects our decision to downgrade (and by how many levels) or not the evidence for inconsistency: the larger the 2 the more likely to downgrade the evidence for the investigated outcome.…”

Section: Discussionmentioning

confidence: 99%

Pattern-mixture model in network meta-analysis of binary missing outcome data: one-stage or two-stage approach?

Spineli

Papadimitropoulou

Kalyvas

2020

Preprint

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Abstract Background Trials with binary outcomes can be synthesised using within-trial exact likelihood or approximate normal likelihood in one-stage or two-stage approaches, respectively. The advantages of the one-stage over the two-stage approach have been documented extensively in the literature. Little is known how these approaches behave in the presence of missing outcome data (MOD) which are ubiquitous in trials. In this work, we compare the one-stage versus two-stage approach via a pattern-mixture model in the network meta-analysis Bayesian framework to handle MOD appropriately. Methods We used 29 published networks to empirically compare the two approaches with respect to the relative treatment effects of several competing interventions and the between-trial variance ( {\tau }^{2} ). We categorised the networks according to the extent and balance of MOD in the included trials. To complement the empirical study, we conducted a simulation study to compare the competing approaches regarding bias and width of the 95% credible interval of the (summary) log odds ratios (OR) and {\tau }^{2} in the presence of moderate and large MOD. Results The empirical study did not reveal any systematic bias between the compared approaches regarding the log OR, but showed systematically larger uncertainty around the log OR under the one-stage approach for networks with at least one small trial or low event risk and moderate MOD. For these networks, the simulation study revealed that the bias in log OR for comparisons with the reference intervention in the network was relatively higher in the two-stage approach. Contrariwise, the bias in log OR for the remaining comparisons was relatively higher in the one-stage approach. Overall, bias increased for large MOD. Furthermore, in these networks, the empirical results revealed slightly higher {\tau }^{2} estimates under the one-stage approach irrespective of the extent of MOD. The one-stage approach also led to less precise log OR and {\tau }^{2} when compared with the two-stage approach for large MOD. Conclusions Due to considerable bias in the log ORs overall, especially for large MOD, none of the competing approaches was superior. Until a more competent model is developed, the researchers may prefer the one-stage approach to handle MOD, while acknowledging its limitations.

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“…We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to rate the overall quality of evidence (high, moderate, low, very low) and provided summary of funding (SoF) Tables. We utilized GRADEpro GDT software (Evidence Prime Inc., Hamilton, Ontario, Canada) to generate the SoF Tables [22].…”

Section: Evidence Ratingmentioning

confidence: 99%

How to increase proximal adenoma detection rate: a meta-analysis comparing water exchange, water immersion and air/CO2 insufflation methods for colonoscopy

Aziz

Sharma

Fatima

et al. 2020

aog

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BackgroundRecent meta-analyses have demonstrated a higher adenoma detection rate using the water exchange method (WE), compared to water immersion (WI) and air/CO 2 insufflation (ACI). Proximal adenomas have a high miss rate owing to their location and appearance. We performed a systematic review and meta-analysis of studies comparing the WE and WI methods to the ACI method, with a primary focus on proximal adenoma detection rate. MethodsThe following databases were searched for our systematic review: Medline, Embase, Cochrane Library, CINAHL, and Web of Sciences. We included both randomized controlled trials and cohort studies. The primary outcome was proximal adenoma detection rate, and secondary outcomes were right adenoma detection rate and cecal intubation rate.Results A total of 12 studies (17 arms) with 5660 patients (2260 ACI, 2281 WE, and 1119 WI) were included. A higher proximal adenoma detection rate (risk ratio [RR] 1.30, 95% confidence interval [CI] 1.11-1.53; P=0.001) and right adenoma detection rate (RR 1.43, 95%CI 1.19-1.71; P≤0.001; I 2 =0%) were noted for the WE group compared to the ACI group. The WI group did not demonstrate a better detection rate of proximal or right adenomas. ConclusionsThe water exchange method for colonoscopy holds promise and should be encouraged in the clinical setting to increase proximal and right adenoma detection rates. This will in turn decrease the incidence of colorectal cancer.

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Using systematic reviews in guideline development: The GRADE approach

Cited by 137 publications

References 68 publications

Using GRADE methodology to assess innovation of new medicinal products in Italy

Using GRADE methodology to assess innovation of new medicinal products in Italy

Pattern-mixture model in network meta-analysis of binary missing outcome data: one-stage or two-stage approach?

How to increase proximal adenoma detection rate: a meta-analysis comparing water exchange, water immersion and air/CO2 insufflation methods for colonoscopy

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