GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-An overview in the context of health decision-making
Por:
Brozek, JL, Canelo-Aybar, C, Akl, EA, Bowen, JM, Bucher, J, Chiu, WA, Cronin, M, 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, JE, Thayer, KA, Tsaioun, K, Vale, L, Wambaugh, J, Wignall, J, Williams, A, Xie, F, Zhang, Y, Schunemann, HJ
Publicada:
1 ene 2021
Ahead of Print:
1 ene 2021
Resumen:
Objectives: The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs).
Study Design and Setting: Expert consultations and an international multidisciplinary 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 modeling community. Feedback from experts in a broad range of modeling 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 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 modeling and health care disciplines.
Conclusion: 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 modeling 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, and health economics). (C) 2020 Published by Elsevier Inc.
Filiaciones:
Brozek, JL:
McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hamilton, ON, Canada
McMaster Univ, Dept Med, Hamilton, ON, Canada
McMaster Univ, McMaster GRADE Ctr, Hamilton, ON, Canada
McMaster Univ, Michael DeGroote Cochrane Canada Ctr, Hamilton, ON, Canada
Canelo-Aybar, C:
Univ Autonoma Barcelona, Dept Paediat Obstet & Gynaecol, Prevent Med & Publ Health, PhD Programme Methodol Biomed Res & Publ Health, Bellaterra, Spain
CIBERESP, Iberoamer Cochrane Ctr, Biomed Res Inst IIB Sant Pau, Barcelona, Spain
Akl, EA:
Amer Univ Beirut, Dept Internal Med, Beirut, Lebanon
Bowen, JM:
McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hamilton, ON, Canada
Toronto Hlth Econ & Technol Assessment THETA Coll, Toronto, ON, Canada
Bucher, J:
NIEHS, Natl Toxicol Program, Durham, NC USA
Chiu, WA:
Texas A&M Univ, Dept Vet Integrat Biosci, College Stn, TX USA
Cronin, M:
Liverpool John Moores Univ, Sch Pharm & Chem, Liverpool, Merseyside, England
Djulbegovic, B:
Univ S Florida, Morsani Coll Med, Ctr Evidence Based Med & Hlth Outcome Res, Tampa, FL USA
Falavigna, M:
Hosp Moinhos Vento, Inst Educ & Res, Porto Alegre, RS, Brazil
Guyatt, GH:
McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hamilton, ON, Canada
McMaster Univ, Dept Med, Hamilton, ON, Canada
McMaster Univ, McMaster GRADE Ctr, Hamilton, ON, Canada
McMaster Univ, Michael DeGroote Cochrane Canada Ctr, Hamilton, ON, Canada
Gordon, AA:
ICF Int, Durham, NC USA
Boon, MH:
Univ Glasgow, Inst Hlth & Wellbeing, Glasgow, Lanark, Scotland
Hutubessy, RCW:
WHO, Dept Immunizat Vaccines & Biol, Geneva, Switzerland
Joore, MA:
Maastricht Univ, Med Ctr, Clin Epidemiol & Med Technol Assessment, Maastricht, Netherlands
Katikireddi, V:
Univ Glasgow, Inst Hlth & Wellbeing, Glasgow, Lanark, Scotland
LaKind, J:
LaKind Associates LLC, Catonsville, MD USA
Univ Maryland, Sch Med, Dept Epidemiol & Publ Hlth, Baltimore, MD USA
Langendam, M:
Univ Amsterdam, Acad Med Ctr, Dept Clin Epidemiol Biostat & Bioinformat, Amsterdam, Netherlands
Manja, V:
McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hamilton, ON, Canada
Univ Calif Davis, Dept Surg, Sacramento, CA 95817 USA
Northern Calif Hlth Care Syst, Dept Med, Dept Vet Affairs, Mather, CA USA
Magnuson, K:
ICF Int, Durham, NC USA
Mathioudakis, AG:
Univ Manchester, Univ Hosp South Manchester, Div Infect Immun & Resp Med, Manchester, Lancs, England
Meerpohl, J:
Univ Freiburg, Med Ctr, Inst Evidence Med, Freiburg, Germany
Cochrane Germany, Freiburg, Germany
Mertz, D:
McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hamilton, ON, Canada
Mezencev, R:
US EPA, Natl Ctr Environm Assessment, Washington, DC 20460 USA
Morgan, R:
McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hamilton, ON, Canada
Morgano, GP:
McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hamilton, ON, Canada
McMaster Univ, McMaster GRADE Ctr, Hamilton, ON, Canada
McMaster Univ, Michael DeGroote Cochrane Canada Ctr, Hamilton, ON, Canada
Mustafa, R:
McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hamilton, ON, Canada
Univ Kansas, Med Ctr, Dept Med, Kansas City, KS 66103 USA
O'Flaherty, M:
Univ Liverpool, Inst Populat Hlth Sci, Liverpool, Merseyside, England
Patlewicz, G:
US EPA, Natl Ctr Computat Toxicol, Durham, NC USA
Riva, JJ:
McMaster Univ, McMaster GRADE Ctr, Hamilton, ON, Canada
McMaster Univ, Michael DeGroote Cochrane Canada Ctr, Hamilton, ON, Canada
McMaster Univ, Dept Family Med, Hamilton, ON, Canada
Posso, M:
CIBERESP, Iberoamer Cochrane Ctr, Biomed Res Inst IIB Sant Pau, Barcelona, Spain
Rooney, A:
NIEHS, Natl Toxicol Program, Durham, NC USA
Schlosser, PM:
US EPA, Natl Ctr Environm Assessment, Washington, DC 20460 USA
Schwartz, L:
McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hamilton, ON, Canada
Shemilt, I:
UCL, Inst Educ, EPPI Ctr, London, England
Tarride, JE:
McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hamilton, ON, Canada
McMaster Univ, Programs Assessment Technol Hlth, Hamilton, ON, Canada
Thayer, KA:
Northern Calif Hlth Care Syst, Dept Med, Dept Vet Affairs, Mather, CA USA
Tsaioun, K:
Johns Hopkins Bloomberg Sch Publ Hlth, Evidence Based Toxicol Collaborat, Baltimore, MD USA
Vale, L:
Newcastle Univ, Inst Hlth & Soc, Hlth Econ Grp, Newcastle Upon Tyne, Tyne & Wear, England
Wambaugh, J:
US EPA, Natl Ctr Computat Toxicol, Durham, NC USA
Wignall, J:
ICF Int, Durham, NC USA
Williams, A:
ICF Int, Durham, NC USA
Xie, F:
McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hamilton, ON, Canada
Zhang, Y:
McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hamilton, ON, Canada
Hlth Qual Ontario, Toronto, ON, Canada
Schunemann, HJ:
McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hamilton, ON, Canada
McMaster Univ, Dept Med, Hamilton, ON, Canada
McMaster Univ, McMaster GRADE Ctr, Hamilton, ON, Canada
McMaster Univ, Michael DeGroote Cochrane Canada Ctr, Hamilton, ON, Canada
Green Accepted
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