SMDM Core Course: Introduction to Medical Decision Analysis (Decision-Analytic Modeling)
Beate Jahn, PhD is an Associate Professor and senior lecturer of Medical Decision Making at UMIT, the Harvard Chan School of Public Health and the Munich University and was a visiting researcher in Canada (PATH, THETA), at BGU, Israel and the Prasanna School of Public Health, India. Dr. Jahn is Vice President of SMDM. She is a dedicated teacher providing short courses at the North American and European Meetings, receiving the award for the best short course at the European meeting in 2012. Dr. Jahn’s research interests include decision-analytic modelling, quality-of-life assessment, decision aids and the translation of modeling results into policy and patient empowerment. Her field research includes evaluations of COVID-19 vaccination, colorectal-cancer screening and patient empowerment through balanced outcomes information, breast-cancer screening/treatment and research on obesity and healthy aging.
Univ.-Prof. Uwe Siebert, MD, MPH, MSc, ScD, is Professor of Public Health, Medical Decision Making and Health Technology Assessment (HTA), Chair of the Department of Public Health, Health Services Research and HTA at UMIT - University for Health Sciences, Medical Informatics and Technology, and Adjunct Professor of Epidemiology and Health Policy & Management at the Harvard Chan School of Public Health.
His research interests include applying evidence-based quantitative, causal and translational methods in public health and medicine in the framework of medical decision making, patient guidance and HTA. His research focuses on cancer, infectious disease, cardiovascular disease, neurological disorders, and others. He has served as President of the Society for Medical Decision Making (SMDM). He teaches epidemiology, causal inference, HTA, health economics, and modeling for academia, industry and health authorities. He has authored more than 400 publications and is Editor of the European Journal of Epidemiology. Further information: htads.org, umit.at/dph, hsph.harvard.edu/uwe-siebert. Twitter: @UweSiebert9.
Format Requirements: This is one of the four core short courses of the permanent SMDM curriculum. The SMDM curriculum is a new initiative of the Society with the goal of having a set of introductory-level core courses in foundational aspects of medical decision making. This effort serves the core mission of the Society to educate its members in key content areas, including decision modeling, cost-effectiveness analysis, the psychology of medical decision making, and shared decision making. This course consists of lectures, interactive group exercises and discussions. Examples of published medical decision analyses will be used to illustrate the fields of application, methodological approaches, results and implications of medical decision analysis. Participants will receive material that goes beyond the course for further self-learning. The intended audience includes researchers from all substance matter fields. This is an introductory course; there are no prerequisites. No laptop is needed. Please bring a simple pocket calculator!
Background: Medical decision making is an essential part of health care. It involves choosing an action after weighing the risks and benefits of the options available to the individual patient or the patient population. While all decisions in health care are made under conditions of uncertainty, the degree of uncertainty depends on the availability, validity, and generalizability of clinical data. Medical decision analysis (or decision-analytic modeling) is a systematic approach to decision making under uncertainty that is used widely in medical decision making, clinical guideline development, and health technology assessment of preventive, diagnostic or therapeutic procedures. It involves combining evidence for different outcomes and from different sources. Outcome parameters may include disease progression, treatment efficacy/effectiveness, safety, quality of life, and individual patient preferences. Sources may include epidemiological studies on the natural history of the disease, randomized clinical trials, observational studies, pharmacoepidemiologic studies, quality of life surveys, risk attitude studies, and others.
By the end of this course, participants will:
1) understand the key concepts and goals of medical decision analysis,
2) know the basic methods of decision tree analysis and Markov modeling and be able to choose the appropriate model type for a given research question
3) understand why and when decision-analytic modeling should be used in clinical evaluation, and
4) be able to critically judge the conclusions derived from a decision-analytic model and know the strengths and limitations and of modeling
This half day course provides an introduction into medical decision analysis a tool for clinical evaluation, benefit-harm analysis and medical decision making. During the course, participants will develop a basic understanding of:
Key concepts, definitions and goals of medical decision analysis
Creating the structure of a decision-analytic model
Measuring benefits, harms, and patient preferences
Application of modeling techniques such as decision trees and Markov models
Perform a medical decision analysis with uncertainty/sensitivity analyses
Translate the results from decision analysis into medical decision making and clinical guidelines
Using practical examples, participants will be guided through the main modeling steps. Examples from the published literature will be discussed to understand the application of modeling techniques to specific decision problems and research questions. Modeling recommendations of the ISPOR-SMDM Joint Modeling Good Research Practices Task Force will be presented to allow participants assessing and judging the quality and validity of decision models. Strengths and limitations of medical decision analysis will be discussed at the end of the course.
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MDM offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health policy development.