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Background and Objective: 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., Methods: 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., Results: 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., Conclusion: This work aims to help methodologists in review teams, technology assessors, guideline panelists, and anyone conducting evidence syntheses using GRADE., (Copyright © 2021. Published by Elsevier Inc.)

Objective: Little is known about how developers and panel members report cost and cost effectiveness considerations in GRADE guideline Evidence-to-Decision (EtD) frameworks. A systematic survey was conducted to explore approaches and factors contributing to variability in economic information reporting., Study Design and Setting: Guideline organization websites were systematically searched to create a convenience sample of guidelines. Reviewers screened published EtD frameworks and generated frequencies of reporting approaches. We used thematic analysis to summarize factors related to variability of economic information reporting., Results: We included 142 guidelines. The overall rate of reporting economic information was high (91%); however, there was variability across completion of predefined EtD Likert-type judgments (70%), noting information as not identified across EtD framework domains (57%), and providing remarks to justify recommendations (38%). Six themes contributing to variability emerged, related to: intervention, population, payor, provider, healthcare resource use, and economic model building factors. Only 2 guidelines performed a GRADE certainty appraisal of economic outcomes., Conclusion: Completing predefined EtD Likert-type judgments, specifically reporting a literature review approach, study selection criteria and economic model building limitations, as well as linking these to recommendation justification remarks are potential areas for improved use, adoption and adaptation of recommendation, and transparency of GRADE EtD frameworks., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Introduction: Professional societies, like many other organizations around the world, have recognized the need to use more rigorous processes to ensure that healthcare recommendations are informed by the best available research evidence. This is the 11th of a series of 14 articles that methodologists and researchers from around the world prepared to advise guideline developers for respiratory and other diseases on how to achieve this goal. For this article, we developed five key questions and updated a review of the literature on moving from evidence to recommendations., Methods: We addressed the following specific questions.What is the strength of a recommendation and what determines the strength? What are the implications of strong and weak recommendations for patients, clinicians, and policy makers? Should guideline panels make recommendations in the face of very low-quality evidence? Under which circumstances should guideline panels make research recommendations? How should recommendations be formulated and presented? We searched PubMed and other databases of methodological studies for existing systematic reviews and relevant methodological research. We did not conduct systematic reviews ourselves. Our conclusions are based on available evidence, consideration of what guideline developers are doing, and pre- and postworkshop discussions., Results and Discussion: The strength of a recommendation reflects the extent to which guideline developers can, across the range of patients for whom the recommendations are intended, be confident that the desirable effects of following the recommendation outweigh the undesirable effects. Four factors influence the strength of a recommendation: the quality of evidence supporting the recommendation, the balance between desirable and undesirable effects, the uncertainty or variability of patient values and preferences, and costs. Strong and weak (also called "conditional") recommendations have distinct implications for patients, clinicians, and policy makers. Adherence to strong recommendations or, in the case of weak (conditional) recommendations, documentation of discussion or shared decision making with a patient, might be used as quality measures or performance indicators. Clinicians desire guidance regardless of the quality of the underlying evidence. Very low-quality evidence should ideally result in either appropriately labeled recommendations (i.e., as based on very low-quality evidence) or a statement that the guideline panel did not reach consensus on the recommendation due to the lack of confidence in the effect estimates. However, guideline panels often have more resources, time, and information than practicing clinicians. Therefore, they may be in a position to use their best judgments to make recommendations even when there is very low-quality evidence, although some guideline developers disagree with this approach and prefer a general approach of not making recommendations in the face of very low-quality evidence. Guideline panels should consider making research recommendations when there is important uncertainty about the desirable and undesirable effects of an intervention, further research could reduce that uncertainty, and the potential benefits and savings of reducing the uncertainty outweigh the potential harms of not making the research recommendation. Recommendations for additional research should be as precise and specific as possible.