May 7: Applied Cost-Effectiveness Modeling with R - In Person at ISPOR 2023

May 7, 2023

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Applied Cost-Effectiveness Modeling with R

Methodological & Statistical Research
4 Hours | Course runs 1 day

This short course is offered in-person at the ISPOR 2023 conference. Separate registration is required. Visit the ISPOR 2023 website to register and learn more.

Sunday, 7 May 2023 | Course runs 1 Day
8:00AM-12:00PM Eastern Daylight Time (EDT) 


Historically, economic models for cost-effectiveness analyses have been developed with specialized commercial software (such as TreeAge) or more commonly with spreadsheet software (almost always Microsoft Excel). More recently there has been increasing interest in using R and other programming languages for cost-effectiveness analysis which can offer advantages regarding the integration of input parameter estimation and model simulation, the evaluation of structural uncertainty, and the quantification of decision uncertainty, among others. Programming languages such as R also facilitate reproducibility of model-based cost-effectiveness analysis which is more relevant than ever given recent calls for increased transparency. While these tools are still relatively new, there is an increased interest in learning opportunities as evidenced by recent tutorials, workshops, and development of open-source software.

In this short course, participants learn how to use R to develop a number of different types of economic models to perform cost-effectiveness analysis. Economic models will include time-homogeneous and time-inhomogeneous Markov cohort models, partitioned survival models, and semi-Markov individual patient simulations. The underlying assumptions of each model type is summarized and the implementation in R is presented in an accessible manner. Participants are asked to modify the models in R (eg, adding health states, use of alternative time-to-event distributions) and run analyses (eg, cost-effectiveness analysis, probabilistic sensitivity analysis, evaluating structural uncertainty, and value of information analysis). To make this interactive aspect of the course as efficient as possible, all participants have access to the GitHub repository prior to the course, containing R code to run the economic models and R Markdown files to explain and reproduce the analyses covered in the course.

Registrants receive a digital course book. Copyright, Trademark and Confidentiality Policies apply.


Jeroen P. Jansen, PhD
Oakland, CA, USA; and
University of California
San Francisco, CA, USA

Devin Incerti, PhD
Head of Data Science
EntityRisk, Inc.
San Francisco, CA, USA

Basic Schedule:

4 Hours | Course runs 1 Day

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