EPIB 676 Advanced Topics in Decision-Analytic Modeling for Health

Table of Contents

Course description

Advanced methods used to model health policy decisions and conduct model-based health technology assessment, both theory and technical applications. Methods covered include: Markov and microsimulation models, optimization, Bayesian model calibration and evaluation, probabilistic sensitivity analyses, and value of information analysis. Application areas include: disease screening, prevention, and treatment, prioritization of clinical research, and policies to avert drug overdose deaths.

Prerequisites: a course in probability, a course in statistics or biostatistics, a course on cost-effectiveness such as PPHS 528, and some programming experience (we will use R). Target audience: The course is targeted towards PhD students and advanced Masters students interested in conducting model-based analyses of health policies and health technologies in research

Coming in Winter 2023

Instructor

W. Alton Russell, PhD

Course materials

The course is still under development, but you can view the most up-to-date materials on Github at https://github.com/altonrus/epib-676