Topics in Medical Decision Making [EWP02]
Monday to Friday (5 full days), final assignment due one week after the course.
Prof. Myriam Hunink, MD PhD, Eline Krijkamp, MSc
An introductory course in Decision Modeling (ESP70 or CE02 or EWP05 or HSPH RDS202 or HSPH RDS280 or HSPH RDS286 or faculty approval of an equivalent course) and basic knowledge of probability and statistics.
- Clinical Epidemiology
Detailed information about this course:
This course deals with intermediate- to advanced level topics in the field of medical decision making. Topics that will be addressed include building decision models, evaluation of diagnostic tests, utility assessment, multi-attribute utility theory, Markov cohort models, microsimulation state-transition models, calibration and validation of models, probabilistic sensitivity analysis, value of information analysis, and behavioral decision making. The course will focus on the practical application of techniques and will include published examples and a computer practicum. Students will learn to apply state-of-the-art modeling methods using freely available open source software to evaluate the comparative effectiveness and cost-effectiveness of health interventions. While the primary emphasis is on application, essential underlying theoretical concepts will also be discussed. During the course you will have the opportunity to work on a decision problem which you select yourself. Many students use the course as a way to start writing a paper on a decision model in the field of their interest.
By the end of the course the student will be able to
- Explain why and when decision modeling is useful and what decision modeling entails.
- Describe how decision modeling can be used for the evaluation of diagnostic tests, preventive interventions and therapeutic interventions.
- Explain the theoretical concepts underlying decision tree analysis, utility assessment, multi-attribute utility theory, Markov cohort models, microsimulation state-transition models, calibration and validation of models, probabilistic sensitivity analysis, and value of information analysis.
- Apply decision modeling methods using open source software.
- Evaluate a self-selected decision problem relevant to the student’s field of expertise using the concepts and techniques discussed in the course.
- Explain the limitations of the discussed techniques and describe alternative modeling techniques that could address these limitations.
Clinical researchers, clinical epidemiologists, decision scientists, public health researchers, those in health technology assessment or value-based healthcare. Participants should have basic knowledge of decision modeling to obtain benefit from this course.
Reduction on fees
PLEASE NOTE THIS DOES NOT APPLY TO THE RESEARCH MASTERS (120 EC points)
50% reduction on our fees for participating institutes of NIHES
This 50% reduction is offered to participants formally appointed at the departments or sections participating in NIHES.
University Medical Center Rotterdam/Erasmus University Rotterdam:
- Department of Epidemiology
- Department of Public Health
- Department of Psychiatry, Section of Medical Psychology and Psychotherapy
- Department of Child and Adolescent Psychiatry/Psychology
- Department of Medical Informatics
- Department of General Practice
- Erasmus School of Health Policy & Management
- Generation R
50% reduction on our fees for NIHES affiliated partners
This 50% reduction is offered to participants formally appointed at the following NIHES affiliated partners:
University Medical Center Rotterdam/Erasmus University Rotterdam, departments of:
- General Paediatrics
- Plastic and Reconstructive Surgery
- Rehabilitation Medicine
UMIT, Department of Public Health, Health Services Research and Health Technology Assessment.
25% reduction on our fees for Erasmus MC employees
For participants formally appointed at all departments of Erasmus MC (except the departments mentioned above).
25% reduction for all (international) PhD students
Upon receipt of your application you will receive a request to upload proof of enrollment as a PhD student.