AM7
DECISION THEORY DEMYSTIFIED
Course Level: Beginner
Overview: This workshop will provide participants with a basic overview on a variety of decision theory topics in fun, intuitive, and clinically relevant ways. It is meant for a beginner audience with no formal training in decision theory. The emphasis will be on gaining intuition and a comfort with important decision science topics like expected value of clinical information, the value of a statistical life, and decision-making under uncertainty. The course will use lecture to introduce topics as well as hands-on activities and clinical case studies to connect theory with practice.
Background: Decision theory topics can be abstract and difficult to understand. Although theory underlies clinical decision science research, it is not always apparent how the underlying frameworks influence our results. This course will enhance a clinician or researcher’s literacy on decisions theory topics and connect theory with real world examples. The aim will be to illustrate not only how theory is applied in practice, but also how underlying assumptions and frameworks can influence results.
Format Requirements: This introductory workshop will use lecture, case studies, and hands-on exercises. There are no prerequisites for this course. We will cover several topics; each with a similar approach, progressing from lecture to participation-based activities using clinical case studies once the class is comfortable with a topic.
Description and Objectives:
By the end of this course participants will:
- Develop intuition on a range of decision theory concepts
- Gain an ability to discuss and describe decision theory topics and how they relate to a participant’s research agenda
- Understand how decision theory influences practical applications in clinical settings including implied assumptions and limitations to theoretical frameworks
Katherine Lofgren, MPH
Harvard University
PhD Student
Ankur Pandya, PhD
Harvard T.H. Chan School of Public Health
Assistant Professor of Health Decision Science