Cohort Modeling in R
Course Description and Objectives:
This course will teach participants how to implement cohort state-transition models (cSTMs) in R. We will first give a conceptual overview of cSTMs and the general structure for their implementation in a programming language. This will be followed by a brief review of good coding practices and how to structure your code in an efficient, transparent and reproducible way. We will demonstrate a cSTM in R, followed by hands-on exercises that will help participants implement a cSTM in R. We will also demonstrate how to conduct a cost-effectiveness analysis (CEA) and how to calculate and visually present epidemiological and health economic outcomes using the cSTM. We will shortly cover the implementation of probabilistic sensitivity analysis (PSM) of a cSTM in R.
Throughout the course, we will highlight good programming principles.
At the end of the course, participants will be able to conduct the following analyses in R:
Construct a cSTMs with time dependency since model start (e.g. age dependency)
Perform model-based CEA using R
Efficiently store the transition dynamics of a cSTM
Visualize the output of a cSTM and CEA using the dampack R package
Understand the steps required to perform PSA on a cSTM
Compute epidemiologicaloutcomes of interest from the model
Appreciate the advantages and challenges of using R in decision-analytic modeling
Work in a transparent and efficient fashion using good coding practices based on the DARTH framework
All materials of this short course will be provided to participants after the course for future use.
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