Lisa Caulley1,2, Myriam G Hunink3,4, Gregory W Randolph5, Jennifer J Shin5. 1. Department of Otolaryngology-Head and Neck Surgery, University of Ottawa, Ottawa, Ontario, Canada; The Ottawa Hospital, Ottawa, Ontario, Canada; The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada. 2. Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands. 3. Department of Epidemiology and Department of Radiology, Erasmus MC, Rotterdam, the Netherlands. 4. Center for Health Decision Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA. 5. Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts, USA.
Abstract
OBJECTIVE: To provide a resource to educate clinical decision makers about the analyses and models that can be employed to support data-driven choices. DATA SOURCES: Published studies and literature regarding decision analysis, decision trees, and models used to support clinical decisions. REVIEW METHODS: Decision models provide insights into the evidence and its implications for those who make choices about clinical care and resource allocation. Decision models are designed to further our understanding and allow exploration of the common problems that we face, with parameters derived from the best available evidence. Analysis of these models demonstrates critical insights and uncertainties surrounding key problems via a readily interpretable yet quantitative format. This 11th installment of the Evidence-Based Medicine in Otolaryngology series thus provides a step-by-step introduction to decision models, their typical framework, and favored approaches to inform data-driven practice for patient-level decisions, as well as comparative assessments of proposed health interventions for larger populations. CONCLUSIONS: Information to support decisions may arise from tools such as decision trees, Markov models, microsimulation models, and dynamic transmission models. These data can help guide choices about competing or alternative approaches to health care. IMPLICATIONS FOR PRACTICE: Methods have been developed to support decisions based on data. Understanding the related techniques may help promote an evidence-based approach to clinical management and policy.
OBJECTIVE: To provide a resource to educate clinical decision makers about the analyses and models that can be employed to support data-driven choices. DATA SOURCES: Published studies and literature regarding decision analysis, decision trees, and models used to support clinical decisions. REVIEW METHODS: Decision models provide insights into the evidence and its implications for those who make choices about clinical care and resource allocation. Decision models are designed to further our understanding and allow exploration of the common problems that we face, with parameters derived from the best available evidence. Analysis of these models demonstrates critical insights and uncertainties surrounding key problems via a readily interpretable yet quantitative format. This 11th installment of the Evidence-Based Medicine in Otolaryngology series thus provides a step-by-step introduction to decision models, their typical framework, and favored approaches to inform data-driven practice for patient-level decisions, as well as comparative assessments of proposed health interventions for larger populations. CONCLUSIONS: Information to support decisions may arise from tools such as decision trees, Markov models, microsimulation models, and dynamic transmission models. These data can help guide choices about competing or alternative approaches to health care. IMPLICATIONS FOR PRACTICE: Methods have been developed to support decisions based on data. Understanding the related techniques may help promote an evidence-based approach to clinical management and policy.
Authors: Shaun J Kilty; Myriam G M Hunink; Lisa Caulley; Eline Krijkamp; Mary-Anne Doyle; Kednapa Thavorn; Fahad Alkherayf; Nick Sahlollbey; Selina X Dong; Jason Quinn; Stephanie Johnson-Obaseki; David Schramm Journal: Pituitary Date: 2022-08-27 Impact factor: 3.599