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Brozek, JL, Canelo-Aybar, C, Akl, EA, Bowen, JM, Bucher, J, Chiu, WA, Cronin, MTD, Djulbegovic, B, Falavigna, M, Guyatt, GH, Gordon, AA, Boon, MH, Hutubessy, RCW, Joore, MA, Katikireddi, V, LaKind, J, Langendam, M, Manja, V, Magnuson, K, Mathioudakis, AG , Meerpohl, J, Mertz, D, Mezencev, R, Morgan, R, Morgano, GP, Mustafa, R, O'Flaherty, M, Patlewicz, G, Riva, JJ, Posso, M, Rooney, A, Schlosser, PM, Schwartz, L, Shemilt, I, Tarride, J-E, Thayer, KA, Tsaioun, K, Vale, L, Wambaugh, J, Wignall, J, Williams, A, Xie, F, Zhang, Y, Schünemann, HJ and GRADE Working Group, (2020) GRADE Guidelines 30: The GRADE Approach to Assessing the Certainty of Modelled Evidence - an Overview in the Context of Health Decision-making. Journal of Clinical Epidemiology. ISSN 0895-4356
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Abstract
OBJECTIVES: To present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modelling studies (i.e. certainty associated with model outputs). STUDY DESIGN AND SETTING: Expert consultations and, an international multi-disciplinary 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 modelling community. Feedback from experts in a broad range of modelling and health care disciplines addressed the content validity of the approach. RESULTS: 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 of 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 modelling and health care disciplines. CONCLUSIONS: 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 modelling 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, health economics).
| Item Type: | Article |
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| Uncontrolled Keywords: | 01 Mathematical Sciences, 11 Medical and Health Sciences |
| Subjects: | R Medicine > RM Therapeutics. Pharmacology |
| Divisions: | Pharmacy & Biomolecular Sciences |
| Publisher: | Elsevier |
| Related URLs: | |
| Date Deposited: | 02 Oct 2020 11:37 |
| Last Modified: | 24 Sep 2021 00:50 |
| DOI or ID number: | 10.1016/j.jclinepi.2020.09.018 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/13778 |
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