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Applied Cost-Effectiveness Modeling with R (in person at ISPOR 2024)
LEVEL: Intermediate
TRACK: Methodological & Statistical Research
LENGTH: 4 Hours
| Course runs 1 day
This short course is offered in-person at the ISPOR 2024 conference. Separate registration is required. Visit the ISPOR 2024 Program page to register and learn more.
Sunday, 5 May 2024 | Course runs 1 Day
08:00-12:00 Eastern Standard Time (EST)
DESCRIPTION
Separate registration required.
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). But 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 will 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 will be summarized and the implementation in R will be presented in an accessible manner. Participants will be 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).
PREREQUISITE: Participants who wish to gain hands-on experience are required to bring their laptops. To make this interactive aspect of the course as efficient as possible, all participants will have access to the GitHub repository prior to the course. It will contain R code to run the economic models and R Markdown files to explain and reproduce the analyses covered in the course.
FACULTY MEMBERS
Jeroen P. Jansen, PhD
PRECISIONheor
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
ISPOR short courses are designed to enhance knowledge and techniques in core health economics and outcomes research (HEOR) topics as well as emerging trends in the field. Short courses offer 4 or 8 hours of premium scientific education and an electronic course book. Active attendee participation combined with our expert faculty creates an immersive and impactful virtual learning experience. Short courses are not recorded and are only available during the live broadcast.
