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:
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Construct a cSTMs with time dependency since model start (e.g. age dependency)
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Perform model-based CEA using R
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Efficiently store the transition dynamics of a cSTM
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Visualize the output of a cSTM and CEA using the dampack R package
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Understand the steps required to perform PSA on a cSTM
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Compute epidemiologicaloutcomes of interest from the model
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Appreciate the advantages and challenges of using R in decision-analytic modeling
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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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James Stahl, MD, CM, MPH
MDM Journal
MDM offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health policy development.