Description & Objectives
By the end of this course, participants will
1) understand the role of decision-analytic modeling in health care
2) know the key concepts of six different modeling approaches
3) be able to describe advantages and disadvantages of different modeling approaches
4) be able to critically discuss model selection
The course starts with a short introduction to decision-analytic modeling. Alternative modeling approaches will then be introduced in five sections, each followed by an interactive discussion.
Session_1: This session covers DT and STMM (cohort simulation), two widely used methods. STMMs are based on a set of health states (state-transition models) and have been applied in decision analyses addressing questions about prevention, diagnosis and treatment of chronic diseases.
Session_2: The application of microsimulation in decision analysis allows investigators to model
individuals and evaluate heterogeneous populations. Approaches range from individual-level state transition models to DES and equation-based models. This session gives a general introduction based on their applications in the social sciences, health care and politics.
Session_3: DES is a microsimulation method in which entities (e.g., patients) interact and compete for resources (e.g., hospital beds or organ transplants). We will cover the primary components of DES such as entities, attributes, resources, and queues.
Session_4: ABM is a relatively new approach to modeling autonomous, interacting agents. The
fundamental feature of an agent is the capability to make independent decisions. ABMs have been used to examine economic issues and questions in the areas of emerging behavior and epidemiology. We will cover the role of agents as active model components.
Session_5: SD is a powerful modeling method that involves both qualitative and quantitative approaches. It takes a "whole system" view, demonstrating how a small change in one part of a system can have major unanticipated effects elsewhere, an aspect that is particularly suitable for healthcare applications.