This course will overview the principles and applications of decision analytic modeling for the purposes of developing clinical policy (e.g., what's the optimal screening method and interval for cervical cancer screening) and evaluating the efficiency (cost effectiveness/cost utility) of health interventions. The course will involve both theoretical and practical aspects. Students will have an opportunity to read more deeply in the history and theoretical underpinnings of decision analysis. However, students will also be expected to learn practical skills in advanced modeling by constructing, debugging, and presenting their own complex decision model. Themes covered in the course will include: a brief history of decision analysis, descriptive and normative theories of decision making, measuring health outcomes with patient-derived and community weighted utility measures, using the QALY and its competitors, Markov modeling, Monte Carlo simulation, using mathematical functions in models, modeling for cost effectiveness analysis, and an introduction to Bayesian approaches in modeling.

Objectives: Understand the theoretical assumptions used in decision modeling. Develop advanced practical modeling skills.