Topics in Medical Decision Making
[ EL004 ]

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.

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.

Assignment(s)

T.B.A.