313.602.01 Economic Evaluation II
- Health Policy and Management
- 2nd term
- 3 credits
- Academic Year:
- 2019 - 2020
- East Baltimore
- Class Times:
- M W, 3:30 - 4:50pm
Economic Evaluation I (313.601.01)
Cost-effectiveness analysis (CEA) is a multidisciplinary science which aims to systematically and rigorously compare health interventions to reach optimal decision-making. Rooted in economic theory, decision science and statistics, CEA (and related methodologies) continue to evolve into a diverse toolkit of techniques that allow us to better quantify costs and effects of healthcare technologies and public health interventions.
Builds on the theory and methods taught in Economic Evaluation I to allow students to gain an understanding of intermediate topics in CEA. Provides students with experience of hands on development of decision trees. Focuses on having students become familiar with best practices in this growing field. Establishes the ability to critically appraise published work and construct simple cost-effectiveness models using Excel and other software. Prepares students for more complex modeling covered in Economic Evaluation III-IV.
- Learning Objectives:
- Identify the key components of CEAs and critically review CEA and related literature
- Construct a decision tree model
- Quantify, visualize and communicate the effects of uncertainty in CEA
- Describe the role of health technology appraisal both within and outside the United States
- Discuss examples of ethical issues that can arise in applying economic evaluation to the allocation of societal health care resources
- Methods of Assessment:
Homework assignments and Labs (20%); class participation and pop quizzes (10%); ICER Critique (30%); midterm (15%); and final exam (25%).
- Enrollment Restriction:
Undergraduate students are not permitted in this course
- Instructor Consent:
Consent required for some students
- Consent Note:
Required if Economic Evaluation I was not completed in term 1 of 2018-2019.
- For consent, contact:
- Jointly Offered With: