GRADE Guidelines 30: The GRADE Approach to Assessing the Certainty of Modelled Evidence - an Overview in the Context of Health Decision-making.
Journal article

GRADE Guidelines 30: The GRADE Approach to Assessing the Certainty of Modelled Evidence - an Overview in the Context of Health Decision-making.

  • Brozek JL Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada; McMaster GRADE Centre & Michael DeGroote Cochrane Canada Centre, McMaster University, Hamilton, Ontario, Canada.
  • Canelo-Aybar C Department of Paediatrics, Obstetrics and Gynaecology, Preventive Medicine, and Public Health. PhD Programme in Methodology of Biomedical Research and Public Health. Universitat Autònoma de Barcelona, Bellaterra, Spain; Iberoamerican Cochrane Center, Biomedical Research Institute (IIB Sant Pau-CIBERESP), Barcelona, Spain.
  • Akl EA Department of Internal Medicine, American University of Beirut, Beirut, Lebanon.
  • Bowen JM Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Toronto Health Economics and Technology Assessment (THETA) Collaborative, Toronto, Ontario, Canada.
  • Bucher J National Toxicology Program, National Institute of Environmental Health Sciences, Durham, North Carolina, USA.
  • Chiu WA Texas A&M University, College Station, Texas, USA.
  • Cronin M Liverpool John Moores University, Liverpool, UK.
  • Djulbegovic B Center for Evidence-Based Medicine and Health Outcome Research, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA.
  • Falavigna M Institute for Education and Research, Hospital Moinhos de Vento, Porto Alegre, Rio Grande do Sul, Brazil.
  • Guyatt GH Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada; McMaster GRADE Centre & Michael DeGroote Cochrane Canada Centre, McMaster University, Hamilton, Ontario, Canada.
  • Gordon AA ICF International, Durham, North Carolina, USA.
  • Boon MH Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK.
  • Hutubessy RCW World Health Organization, Geneva, Switzerland.
  • Joore MA Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, the Netherlands.
  • Katikireddi V Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK.
  • LaKind J LaKind Associates, LLC, Catonsville, Maryland, USA; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA.
  • Langendam M Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
  • Manja V Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Surgery, University of California Davis, Sacramento, California, USA; Department of Medicine, Department of Veterans Affairs, Northern California Health Care System, Mather, California, USA.
  • Magnuson K ICF International, Durham, North Carolina, USA.
  • Mathioudakis AG Division of Infection, Immunity and Respiratory Medicine, University Hospital of South Manchester, University of Manchester, Manchester, UK.
  • Meerpohl J Institute for Evidence in Medicine, Medical Center, University of Freiburg, Freiburg-am-Breisgau, Germany; Cochrane Germany, Freiburg-am-Breisgau, Germany.
  • Mertz D Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
  • Mezencev R National Center for Environmental Assessment, U.S. Environmental Protection Agency, Washington, D.C., District of Columbia, USA.
  • Morgan R Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
  • Morgano GP Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; McMaster GRADE Centre & Michael DeGroote Cochrane Canada Centre, McMaster University, Hamilton, Ontario, Canada.
  • Mustafa R Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA.
  • O'Flaherty M Institute of Population Health Sciences, University of Liverpool, Liverpool, UK.
  • Patlewicz G National Center for Computational Toxicology, U.S. Environmental Protection Agency, Durham, North Carolina, USA.
  • Riva JJ McMaster GRADE Centre & Michael DeGroote Cochrane Canada Centre, McMaster University, Hamilton, Ontario, Canada; Department of Family Medicine, McMaster University, Hamilton, Ontario, Canada.
  • Posso M Iberoamerican Cochrane Center, Biomedical Research Institute (IIB Sant Pau-CIBERESP), Barcelona, Spain.
  • Rooney A National Toxicology Program, National Institute of Environmental Health Sciences, Durham, North Carolina, USA.
  • Schlosser PM National Center for Environmental Assessment, U.S. Environmental Protection Agency, Washington, D.C., District of Columbia, USA.
  • Schwartz L Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
  • Shemilt I EPPI-Centre, Institute of Education, University College London, London, UK.
  • Tarride JE Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Programs for Assessment of Technology in Health, McMaster University, Hamilton, Ontario, Canada.
  • Thayer KA Department of Medicine, Department of Veterans Affairs, Northern California Health Care System, Mather, California, USA.
  • Tsaioun K Evidence-Based Toxicology Collaboration, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
  • Vale L Health Economics Group, Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK.
  • Wambaugh J National Center for Computational Toxicology, U.S. Environmental Protection Agency, Durham, North Carolina, USA.
  • Wignall J ICF International, Durham, North Carolina, USA.
  • Williams A ICF International, Durham, North Carolina, USA.
  • Xie F Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
  • Zhang Y Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Health Quality Ontario, Toronto, Ontario, Canada.
  • Schünemann HJ Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada; McMaster GRADE Centre & Michael DeGroote Cochrane Canada Centre, McMaster University, Hamilton, Ontario, Canada.
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  • 2020-09-27
Published in:
  • Journal of clinical epidemiology. - 2020
English 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).
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://folia.unifr.ch/global/documents/40100
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