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. model outputs). Study Design and Setting: We performed expert consultations, held an international multi-disciplinary workshop, and further elaborated a conceptual approach for assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. We assessed the content validity of the approach obtaining feedback from experts in a broad range of modelling and health care disciplines.
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 in the context of assessing the certainty of evidence from models. The assessment itself will depend on the nature of model inputs and the model itself. We distinguished the assessment of the certainty of model outputs from a single model and across multiple models. We proposed a framework for selecting the best available evidence from models: 1) to develop de novo a model specific to the situation of interest, 2) to identify an existing model the outputs of which provide the highest certainty evidence for the situation of interest, that could be used either “off the shelf” or after adaptation, and 3) to use outputs from multiple models. We also present a summary of preferred terminology to
facilitate communication among various 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 continue developing detailed methods and related guidance for the assessment of specific domains determining the certainty of evidence from models.
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 in the context of assessing the certainty of evidence from models. The assessment itself will depend on the nature of model inputs and the model itself. We distinguished the assessment of the certainty of model outputs from a single model and across multiple models. We proposed a framework for selecting the best available evidence from models: 1) to develop de novo a model specific to the situation of interest, 2) to identify an existing model the outputs of which provide the highest certainty evidence for the situation of interest, that could be used either “off the shelf” or after adaptation, and 3) to use outputs from multiple models. We also present a summary of preferred terminology to
facilitate communication among various 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 continue developing detailed methods and related guidance for the assessment of specific domains determining the certainty of evidence from models.
| Original language | English |
|---|---|
| Journal | Journal of Clinical Epidemiology |
| Publication status | Accepted/In press - 17 Sept 2020 |