GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making
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 co...
Ausführliche Beschreibung
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of clinical epidemiology - Amsterdam [u.a.] : Elsevier Science, 1988, 129 |
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| Übergeordnetes Werk: |
volume:129 |
| DOI / URN: |
10.1016/j.jclinepi.2020.09.018 |
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ELV005332001 |
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| 024 | 7 | |a 10.1016/j.jclinepi.2020.09.018 |2 doi | |
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| 100 | 1 | |a Brozek, Jan L. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making |
| 264 | 1 | |c 2020 | |
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| 520 | |a 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). | ||
| 650 | 4 | |a GRADE | |
| 650 | 4 | |a Certainty of evidence | |
| 650 | 4 | |a Mathematical models | |
| 650 | 4 | |a Modelling studies | |
| 650 | 4 | |a Health care Decision making | |
| 650 | 4 | |a Guidelines | |
| 700 | 1 | |a Canelo-Aybar, Carlos |e verfasserin |4 aut | |
| 700 | 1 | |a Akl, Elie A. |e verfasserin |4 aut | |
| 700 | 1 | |a Bowen, James M. |e verfasserin |4 aut | |
| 700 | 1 | |a Bucher, John |e verfasserin |0 (orcid)0000-0002-6795-8518 |4 aut | |
| 700 | 1 | |a Chiu, Weihsueh A. |e verfasserin |4 aut | |
| 700 | 1 | |a Cronin, Mark |e verfasserin |4 aut | |
| 700 | 1 | |a Djulbegovic, Benjamin |e verfasserin |4 aut | |
| 700 | 1 | |a Falavigna, Maicon |e verfasserin |4 aut | |
| 700 | 1 | |a Guyatt, Gordon H. |e verfasserin |4 aut | |
| 700 | 1 | |a Gordon, Ami A. |e verfasserin |4 aut | |
| 700 | 1 | |a Hilton Boon, Michele |e verfasserin |4 aut | |
| 700 | 1 | |a Hutubessy, Raymond C.W. |e verfasserin |4 aut | |
| 700 | 1 | |a Joore, Manuela A. |e verfasserin |4 aut | |
| 700 | 1 | |a Katikireddi, Vittal |e verfasserin |4 aut | |
| 700 | 1 | |a LaKind, Judy |e verfasserin |4 aut | |
| 700 | 1 | |a Langendam, Miranda |e verfasserin |4 aut | |
| 700 | 1 | |a Manja, Veena |e verfasserin |4 aut | |
| 700 | 1 | |a Magnuson, Kristen |e verfasserin |4 aut | |
| 700 | 1 | |a Mathioudakis, Alexander G. |e verfasserin |4 aut | |
| 700 | 1 | |a Meerpohl, Joerg |e verfasserin |4 aut | |
| 700 | 1 | |a Mertz, Dominik |e verfasserin |4 aut | |
| 700 | 1 | |a Mezencev, Roman |e verfasserin |4 aut | |
| 700 | 1 | |a Morgan, Rebecca |e verfasserin |0 (orcid)0000-0002-1012-4897 |4 aut | |
| 700 | 1 | |a Morgano, Gian Paolo |e verfasserin |4 aut | |
| 700 | 1 | |a Mustafa, Reem |e verfasserin |4 aut | |
| 700 | 1 | |a O'Flaherty, Martin |e verfasserin |4 aut | |
| 700 | 1 | |a Patlewicz, Grace |e verfasserin |0 (orcid)0000-0003-3863-9689 |4 aut | |
| 700 | 1 | |a Riva, John J. |e verfasserin |4 aut | |
| 700 | 1 | |a Posso, Margarita |e verfasserin |4 aut | |
| 700 | 1 | |a Rooney, Andrew |e verfasserin |4 aut | |
| 700 | 1 | |a Schlosser, Paul M. |e verfasserin |4 aut | |
| 700 | 1 | |a Schwartz, Lisa |e verfasserin |4 aut | |
| 700 | 1 | |a Shemilt, Ian |e verfasserin |4 aut | |
| 700 | 1 | |a Tarride, Jean-Eric |e verfasserin |4 aut | |
| 700 | 1 | |a Thayer, Kristina A. |e verfasserin |4 aut | |
| 700 | 1 | |a Tsaioun, Katya |e verfasserin |4 aut | |
| 700 | 1 | |a Vale, Luke |e verfasserin |0 (orcid)0000-0001-8574-8429 |4 aut | |
| 700 | 1 | |a Wambaugh, John |e verfasserin |4 aut | |
| 700 | 1 | |a Wignall, Jessica |e verfasserin |4 aut | |
| 700 | 1 | |a Williams, Ashley |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Feng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Schünemann, Holger J. |e verfasserin |4 aut | |
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10.1016/j.jclinepi.2020.09.018 doi (DE-627)ELV005332001 (ELSEVIER)S0895-4356(20)31103-3 DE-627 ger DE-627 rda eng 610 DE-600 PHARM DE-84 fid 44.11 bkl 44.40 bkl Brozek, Jan L. verfasserin aut GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). GRADE Certainty of evidence Mathematical models Modelling studies Health care Decision making Guidelines Canelo-Aybar, Carlos verfasserin aut Akl, Elie A. verfasserin aut Bowen, James M. verfasserin aut Bucher, John verfasserin (orcid)0000-0002-6795-8518 aut Chiu, Weihsueh A. verfasserin aut Cronin, Mark verfasserin aut Djulbegovic, Benjamin verfasserin aut Falavigna, Maicon verfasserin aut Guyatt, Gordon H. verfasserin aut Gordon, Ami A. verfasserin aut Hilton Boon, Michele verfasserin aut Hutubessy, Raymond C.W. verfasserin aut Joore, Manuela A. verfasserin aut Katikireddi, Vittal verfasserin aut LaKind, Judy verfasserin aut Langendam, Miranda verfasserin aut Manja, Veena verfasserin aut Magnuson, Kristen verfasserin aut Mathioudakis, Alexander G. verfasserin aut Meerpohl, Joerg verfasserin aut Mertz, Dominik verfasserin aut Mezencev, Roman verfasserin aut Morgan, Rebecca verfasserin (orcid)0000-0002-1012-4897 aut Morgano, Gian Paolo verfasserin aut Mustafa, Reem verfasserin aut O'Flaherty, Martin verfasserin aut Patlewicz, Grace verfasserin (orcid)0000-0003-3863-9689 aut Riva, John J. verfasserin aut Posso, Margarita verfasserin aut Rooney, Andrew verfasserin aut Schlosser, Paul M. verfasserin aut Schwartz, Lisa verfasserin aut Shemilt, Ian verfasserin aut Tarride, Jean-Eric verfasserin aut Thayer, Kristina A. verfasserin aut Tsaioun, Katya verfasserin aut Vale, Luke verfasserin (orcid)0000-0001-8574-8429 aut Wambaugh, John verfasserin aut Wignall, Jessica verfasserin aut Williams, Ashley verfasserin aut Xie, Feng verfasserin aut Zhang, Yuan verfasserin aut Schünemann, Holger J. verfasserin aut Enthalten in Journal of clinical epidemiology Amsterdam [u.a.] : Elsevier Science, 1988 129 Online-Ressource (DE-627)306659700 (DE-600)1500490-9 (DE-576)081986343 1878-5921 nnns volume:129 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-PHARM SSG-OLC-PHA SSG-OPC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.11 Präventivmedizin 44.40 Pharmazie Pharmazeutika AR 129 |
| spelling |
10.1016/j.jclinepi.2020.09.018 doi (DE-627)ELV005332001 (ELSEVIER)S0895-4356(20)31103-3 DE-627 ger DE-627 rda eng 610 DE-600 PHARM DE-84 fid 44.11 bkl 44.40 bkl Brozek, Jan L. verfasserin aut GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). GRADE Certainty of evidence Mathematical models Modelling studies Health care Decision making Guidelines Canelo-Aybar, Carlos verfasserin aut Akl, Elie A. verfasserin aut Bowen, James M. verfasserin aut Bucher, John verfasserin (orcid)0000-0002-6795-8518 aut Chiu, Weihsueh A. verfasserin aut Cronin, Mark verfasserin aut Djulbegovic, Benjamin verfasserin aut Falavigna, Maicon verfasserin aut Guyatt, Gordon H. verfasserin aut Gordon, Ami A. verfasserin aut Hilton Boon, Michele verfasserin aut Hutubessy, Raymond C.W. verfasserin aut Joore, Manuela A. verfasserin aut Katikireddi, Vittal verfasserin aut LaKind, Judy verfasserin aut Langendam, Miranda verfasserin aut Manja, Veena verfasserin aut Magnuson, Kristen verfasserin aut Mathioudakis, Alexander G. verfasserin aut Meerpohl, Joerg verfasserin aut Mertz, Dominik verfasserin aut Mezencev, Roman verfasserin aut Morgan, Rebecca verfasserin (orcid)0000-0002-1012-4897 aut Morgano, Gian Paolo verfasserin aut Mustafa, Reem verfasserin aut O'Flaherty, Martin verfasserin aut Patlewicz, Grace verfasserin (orcid)0000-0003-3863-9689 aut Riva, John J. verfasserin aut Posso, Margarita verfasserin aut Rooney, Andrew verfasserin aut Schlosser, Paul M. verfasserin aut Schwartz, Lisa verfasserin aut Shemilt, Ian verfasserin aut Tarride, Jean-Eric verfasserin aut Thayer, Kristina A. verfasserin aut Tsaioun, Katya verfasserin aut Vale, Luke verfasserin (orcid)0000-0001-8574-8429 aut Wambaugh, John verfasserin aut Wignall, Jessica verfasserin aut Williams, Ashley verfasserin aut Xie, Feng verfasserin aut Zhang, Yuan verfasserin aut Schünemann, Holger J. verfasserin aut Enthalten in Journal of clinical epidemiology Amsterdam [u.a.] : Elsevier Science, 1988 129 Online-Ressource (DE-627)306659700 (DE-600)1500490-9 (DE-576)081986343 1878-5921 nnns volume:129 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-PHARM SSG-OLC-PHA SSG-OPC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.11 Präventivmedizin 44.40 Pharmazie Pharmazeutika AR 129 |
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10.1016/j.jclinepi.2020.09.018 doi (DE-627)ELV005332001 (ELSEVIER)S0895-4356(20)31103-3 DE-627 ger DE-627 rda eng 610 DE-600 PHARM DE-84 fid 44.11 bkl 44.40 bkl Brozek, Jan L. verfasserin aut GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). GRADE Certainty of evidence Mathematical models Modelling studies Health care Decision making Guidelines Canelo-Aybar, Carlos verfasserin aut Akl, Elie A. verfasserin aut Bowen, James M. verfasserin aut Bucher, John verfasserin (orcid)0000-0002-6795-8518 aut Chiu, Weihsueh A. verfasserin aut Cronin, Mark verfasserin aut Djulbegovic, Benjamin verfasserin aut Falavigna, Maicon verfasserin aut Guyatt, Gordon H. verfasserin aut Gordon, Ami A. verfasserin aut Hilton Boon, Michele verfasserin aut Hutubessy, Raymond C.W. verfasserin aut Joore, Manuela A. verfasserin aut Katikireddi, Vittal verfasserin aut LaKind, Judy verfasserin aut Langendam, Miranda verfasserin aut Manja, Veena verfasserin aut Magnuson, Kristen verfasserin aut Mathioudakis, Alexander G. verfasserin aut Meerpohl, Joerg verfasserin aut Mertz, Dominik verfasserin aut Mezencev, Roman verfasserin aut Morgan, Rebecca verfasserin (orcid)0000-0002-1012-4897 aut Morgano, Gian Paolo verfasserin aut Mustafa, Reem verfasserin aut O'Flaherty, Martin verfasserin aut Patlewicz, Grace verfasserin (orcid)0000-0003-3863-9689 aut Riva, John J. verfasserin aut Posso, Margarita verfasserin aut Rooney, Andrew verfasserin aut Schlosser, Paul M. verfasserin aut Schwartz, Lisa verfasserin aut Shemilt, Ian verfasserin aut Tarride, Jean-Eric verfasserin aut Thayer, Kristina A. verfasserin aut Tsaioun, Katya verfasserin aut Vale, Luke verfasserin (orcid)0000-0001-8574-8429 aut Wambaugh, John verfasserin aut Wignall, Jessica verfasserin aut Williams, Ashley verfasserin aut Xie, Feng verfasserin aut Zhang, Yuan verfasserin aut Schünemann, Holger J. verfasserin aut Enthalten in Journal of clinical epidemiology Amsterdam [u.a.] : Elsevier Science, 1988 129 Online-Ressource (DE-627)306659700 (DE-600)1500490-9 (DE-576)081986343 1878-5921 nnns volume:129 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-PHARM SSG-OLC-PHA SSG-OPC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.11 Präventivmedizin 44.40 Pharmazie Pharmazeutika AR 129 |
| allfieldsGer |
10.1016/j.jclinepi.2020.09.018 doi (DE-627)ELV005332001 (ELSEVIER)S0895-4356(20)31103-3 DE-627 ger DE-627 rda eng 610 DE-600 PHARM DE-84 fid 44.11 bkl 44.40 bkl Brozek, Jan L. verfasserin aut GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). GRADE Certainty of evidence Mathematical models Modelling studies Health care Decision making Guidelines Canelo-Aybar, Carlos verfasserin aut Akl, Elie A. verfasserin aut Bowen, James M. verfasserin aut Bucher, John verfasserin (orcid)0000-0002-6795-8518 aut Chiu, Weihsueh A. verfasserin aut Cronin, Mark verfasserin aut Djulbegovic, Benjamin verfasserin aut Falavigna, Maicon verfasserin aut Guyatt, Gordon H. verfasserin aut Gordon, Ami A. verfasserin aut Hilton Boon, Michele verfasserin aut Hutubessy, Raymond C.W. verfasserin aut Joore, Manuela A. verfasserin aut Katikireddi, Vittal verfasserin aut LaKind, Judy verfasserin aut Langendam, Miranda verfasserin aut Manja, Veena verfasserin aut Magnuson, Kristen verfasserin aut Mathioudakis, Alexander G. verfasserin aut Meerpohl, Joerg verfasserin aut Mertz, Dominik verfasserin aut Mezencev, Roman verfasserin aut Morgan, Rebecca verfasserin (orcid)0000-0002-1012-4897 aut Morgano, Gian Paolo verfasserin aut Mustafa, Reem verfasserin aut O'Flaherty, Martin verfasserin aut Patlewicz, Grace verfasserin (orcid)0000-0003-3863-9689 aut Riva, John J. verfasserin aut Posso, Margarita verfasserin aut Rooney, Andrew verfasserin aut Schlosser, Paul M. verfasserin aut Schwartz, Lisa verfasserin aut Shemilt, Ian verfasserin aut Tarride, Jean-Eric verfasserin aut Thayer, Kristina A. verfasserin aut Tsaioun, Katya verfasserin aut Vale, Luke verfasserin (orcid)0000-0001-8574-8429 aut Wambaugh, John verfasserin aut Wignall, Jessica verfasserin aut Williams, Ashley verfasserin aut Xie, Feng verfasserin aut Zhang, Yuan verfasserin aut Schünemann, Holger J. verfasserin aut Enthalten in Journal of clinical epidemiology Amsterdam [u.a.] : Elsevier Science, 1988 129 Online-Ressource (DE-627)306659700 (DE-600)1500490-9 (DE-576)081986343 1878-5921 nnns volume:129 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-PHARM SSG-OLC-PHA SSG-OPC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.11 Präventivmedizin 44.40 Pharmazie Pharmazeutika AR 129 |
| allfieldsSound |
10.1016/j.jclinepi.2020.09.018 doi (DE-627)ELV005332001 (ELSEVIER)S0895-4356(20)31103-3 DE-627 ger DE-627 rda eng 610 DE-600 PHARM DE-84 fid 44.11 bkl 44.40 bkl Brozek, Jan L. verfasserin aut GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). GRADE Certainty of evidence Mathematical models Modelling studies Health care Decision making Guidelines Canelo-Aybar, Carlos verfasserin aut Akl, Elie A. verfasserin aut Bowen, James M. verfasserin aut Bucher, John verfasserin (orcid)0000-0002-6795-8518 aut Chiu, Weihsueh A. verfasserin aut Cronin, Mark verfasserin aut Djulbegovic, Benjamin verfasserin aut Falavigna, Maicon verfasserin aut Guyatt, Gordon H. verfasserin aut Gordon, Ami A. verfasserin aut Hilton Boon, Michele verfasserin aut Hutubessy, Raymond C.W. verfasserin aut Joore, Manuela A. verfasserin aut Katikireddi, Vittal verfasserin aut LaKind, Judy verfasserin aut Langendam, Miranda verfasserin aut Manja, Veena verfasserin aut Magnuson, Kristen verfasserin aut Mathioudakis, Alexander G. verfasserin aut Meerpohl, Joerg verfasserin aut Mertz, Dominik verfasserin aut Mezencev, Roman verfasserin aut Morgan, Rebecca verfasserin (orcid)0000-0002-1012-4897 aut Morgano, Gian Paolo verfasserin aut Mustafa, Reem verfasserin aut O'Flaherty, Martin verfasserin aut Patlewicz, Grace verfasserin (orcid)0000-0003-3863-9689 aut Riva, John J. verfasserin aut Posso, Margarita verfasserin aut Rooney, Andrew verfasserin aut Schlosser, Paul M. verfasserin aut Schwartz, Lisa verfasserin aut Shemilt, Ian verfasserin aut Tarride, Jean-Eric verfasserin aut Thayer, Kristina A. verfasserin aut Tsaioun, Katya verfasserin aut Vale, Luke verfasserin (orcid)0000-0001-8574-8429 aut Wambaugh, John verfasserin aut Wignall, Jessica verfasserin aut Williams, Ashley verfasserin aut Xie, Feng verfasserin aut Zhang, Yuan verfasserin aut Schünemann, Holger J. verfasserin aut Enthalten in Journal of clinical epidemiology Amsterdam [u.a.] : Elsevier Science, 1988 129 Online-Ressource (DE-627)306659700 (DE-600)1500490-9 (DE-576)081986343 1878-5921 nnns volume:129 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-PHARM SSG-OLC-PHA SSG-OPC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.11 Präventivmedizin 44.40 Pharmazie Pharmazeutika AR 129 |
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Brozek, Jan L. @@aut@@ Canelo-Aybar, Carlos @@aut@@ Akl, Elie A. @@aut@@ Bowen, James M. @@aut@@ Bucher, John @@aut@@ Chiu, Weihsueh A. @@aut@@ Cronin, Mark @@aut@@ Djulbegovic, Benjamin @@aut@@ Falavigna, Maicon @@aut@@ Guyatt, Gordon H. @@aut@@ Gordon, Ami A. @@aut@@ Hilton Boon, Michele @@aut@@ Hutubessy, Raymond C.W. @@aut@@ Joore, Manuela A. @@aut@@ Katikireddi, Vittal @@aut@@ LaKind, Judy @@aut@@ Langendam, Miranda @@aut@@ Manja, Veena @@aut@@ Magnuson, Kristen @@aut@@ Mathioudakis, Alexander G. @@aut@@ Meerpohl, Joerg @@aut@@ Mertz, Dominik @@aut@@ Mezencev, Roman @@aut@@ Morgan, Rebecca @@aut@@ Morgano, Gian Paolo @@aut@@ Mustafa, Reem @@aut@@ O'Flaherty, Martin @@aut@@ Patlewicz, Grace @@aut@@ Riva, John J. @@aut@@ Posso, Margarita @@aut@@ Rooney, Andrew @@aut@@ Schlosser, Paul M. @@aut@@ Schwartz, Lisa @@aut@@ Shemilt, Ian @@aut@@ Tarride, Jean-Eric @@aut@@ Thayer, Kristina A. @@aut@@ Tsaioun, Katya @@aut@@ Vale, Luke @@aut@@ Wambaugh, John @@aut@@ Wignall, Jessica @@aut@@ Williams, Ashley @@aut@@ Xie, Feng @@aut@@ Zhang, Yuan @@aut@@ Schünemann, Holger J. @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV005332001</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524162720.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230504s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jclinepi.2020.09.018</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV005332001</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0895-4356(20)31103-3</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">PHARM</subfield><subfield code="q">DE-84</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.11</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.40</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Brozek, Jan L.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. 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| author |
Brozek, Jan L. |
| spellingShingle |
Brozek, Jan L. ddc 610 fid PHARM bkl 44.11 bkl 44.40 misc GRADE misc Certainty of evidence misc Mathematical models misc Modelling studies misc Health care Decision making misc Guidelines GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making |
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Brozek, Jan L. |
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electronic Article |
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610 - Medicine & health |
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keep |
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aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut |
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1878-5921 |
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610 DE-600 PHARM DE-84 fid 44.11 bkl 44.40 bkl GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making GRADE Certainty of evidence Mathematical models Modelling studies Health care Decision making Guidelines |
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ddc 610 fid PHARM bkl 44.11 bkl 44.40 misc GRADE misc Certainty of evidence misc Mathematical models misc Modelling studies misc Health care Decision making misc Guidelines |
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ddc 610 fid PHARM bkl 44.11 bkl 44.40 misc GRADE misc Certainty of evidence misc Mathematical models misc Modelling studies misc Health care Decision making misc Guidelines |
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GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making |
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GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making |
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Brozek, Jan L. |
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Brozek, Jan L. Canelo-Aybar, Carlos Akl, Elie A. Bowen, James M. Bucher, John Chiu, Weihsueh A. Cronin, Mark Djulbegovic, Benjamin Falavigna, Maicon Guyatt, Gordon H. Gordon, Ami A. Hilton Boon, Michele Hutubessy, Raymond C.W. Joore, Manuela A. Katikireddi, Vittal LaKind, Judy Langendam, Miranda Manja, Veena Magnuson, Kristen Mathioudakis, Alexander G. Meerpohl, Joerg Mertz, Dominik Mezencev, Roman Morgan, Rebecca Morgano, Gian Paolo Mustafa, Reem O'Flaherty, Martin Patlewicz, Grace Riva, John J. Posso, Margarita Rooney, Andrew Schlosser, Paul M. Schwartz, Lisa Shemilt, Ian Tarride, Jean-Eric Thayer, Kristina A. Tsaioun, Katya Vale, Luke Wambaugh, John Wignall, Jessica Williams, Ashley Xie, Feng Zhang, Yuan Schünemann, Holger J. |
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10.1016/j.jclinepi.2020.09.018 |
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grade guidelines 30: the grade approach to assessing the certainty of modeled evidence—an overview in the context of health decision-making |
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GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making |
| abstract |
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). |
| abstractGer |
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). |
| abstract_unstemmed |
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). |
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GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making |
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Canelo-Aybar, Carlos Akl, Elie A. Bowen, James M. Bucher, John Chiu, Weihsueh A. Cronin, Mark Djulbegovic, Benjamin Falavigna, Maicon Guyatt, Gordon H. Gordon, Ami A. Hilton Boon, Michele Hutubessy, Raymond C.W. Joore, Manuela A. Katikireddi, Vittal LaKind, Judy Langendam, Miranda Manja, Veena Magnuson, Kristen Mathioudakis, Alexander G. Meerpohl, Joerg Mertz, Dominik Mezencev, Roman Morgan, Rebecca Morgano, Gian Paolo Mustafa, Reem O'Flaherty, Martin Patlewicz, Grace Riva, John J. Posso, Margarita Rooney, Andrew Schlosser, Paul M. Schwartz, Lisa Shemilt, Ian Tarride, Jean-Eric Thayer, Kristina A. Tsaioun, Katya Vale, Luke Wambaugh, John Wignall, Jessica Williams, Ashley Xie, Feng Zhang, Yuan Schünemann, Holger J. |
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Canelo-Aybar, Carlos Akl, Elie A. Bowen, James M. Bucher, John Chiu, Weihsueh A. Cronin, Mark Djulbegovic, Benjamin Falavigna, Maicon Guyatt, Gordon H. Gordon, Ami A. Hilton Boon, Michele Hutubessy, Raymond C.W. Joore, Manuela A. Katikireddi, Vittal LaKind, Judy Langendam, Miranda Manja, Veena Magnuson, Kristen Mathioudakis, Alexander G. Meerpohl, Joerg Mertz, Dominik Mezencev, Roman Morgan, Rebecca Morgano, Gian Paolo Mustafa, Reem O'Flaherty, Martin Patlewicz, Grace Riva, John J. Posso, Margarita Rooney, Andrew Schlosser, Paul M. Schwartz, Lisa Shemilt, Ian Tarride, Jean-Eric Thayer, Kristina A. Tsaioun, Katya Vale, Luke Wambaugh, John Wignall, Jessica Williams, Ashley Xie, Feng Zhang, Yuan Schünemann, Holger J. |
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| score |
7.3964148 |