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Use R for your economic models and gain in transparency and time!

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). Join others who have implemented R and other programming languages for cost-effectiveness analysis which offer these major advantages:

• Integration of input parameter estimation and model simulation.
• Evaluation of structural uncertainty.
• Quantification of decision uncertainty.

Programming languages such as R facilitate reproducibility of model-based cost-effectiveness analysis which is more relevant than ever given recent calls for increased transparency.

Enroll into this course to:

• Learn how to use R to develop a number of different types of economic models to perform cost-effectiveness analysis.
• Explore economic models that include time-homogeneous and time-inhomogeneous Markov cohort models, partitioned survival models, and semi-Markov individual patient simulations.
• Discuss the underlying assumptions of each model type.
• Learn how to implement these models in R, presented in an accessible manner.
• Learn how 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).

This short course is offered in-person at the ISPOR Europe 2024 conference.