Cost-Effectiveness and Decision Modeling using R is a workshop aimed at researchers that are interested on cost-effectiveness and decision modeling using R.

Background

Healthcare resources are limited and policymakers are under increased pressure to make use of these resources as efficiently as possible. Cost-effectiveness analysis and decision analysis are quantitative decision-making frameworks used to formalize objectives, quantify tradeoffs, and ultimately support more informed decision making. These techniques have been applied to a wide range of health policy questions, including optimal cancer screening and treatment guidelines; technology reimbursement and coverage decisions; and hospital operations management.

R is an open-source software that provides a flexible environment where advanced statistical analyses can be combined with decision models of varying complexity within the same framework and the results can be presented in publication ready tabular and graphical forms. The fact that R is freely available also improves model transparency and reproducibility.

The DARTH workgroup is a multi-institutional, multi-university collaborative effort comprised of researchers who have a passion for transparent and open-source solutions to decision analysis in health. The aim of this collaboration is to expand knowledge and develop educational materials that empower people to construct R-based decision models.

Course description

In this course, we will cover the principles of cost-effectiveness analysis and decision analytic modeling and will guide participants in implementing decision trees, Markov models, and microsimulation models in R as well as how to use models for cost-effectiveness analysis and deterministic and probabilistic sensitivity analysis.

Participants will be expected to have some experience with decision modeling and/or basic concepts of economic evaluation. A self-paced module introducing R concepts specifically for decision modeling will be provided as part of your workshop registration.

August 23: Introduction to R for decision modelers August 24: Introduction to cost-effectiveness and decision trees August 25: Markov models August 26: Microsimulation modeling August 27: Sensitivity analysis