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Introduction to Building and Calibrating Simulation Models in R
Date: 3 Decemeber 2023
Presenter: Prof. Jeremy Goldhaber-Fiebert, Professor of Health Policy at Stanford University
The workshop covers the main aspect of constructing and calibrating decision models using R. The workshop assumes some familiarity with concepts of decision-analytic simulation models but is aimed at researchers interested in learning to implement simulation models using R software and fitting them via calibration to data.
Reasons for modeling in R: Availability of excellent, high performance free tools like R and RStudio. Excellent packages for statistical and data manipulation tasks, frameworks like DARTH for decision modeling, and other packages for optimization and calibration. Easy to distribute and reproduce models transparently which is increasingly becoming the standard for submissions to health technology assessment organizations as well as for publication and dissemination.
Outline:
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Brief review of the reasons for modeling in R
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Brief review of basic R functions that are commonly used in decision modelling (import/export data, data handling, basic distributions, “if” and “for” loops etc.) will be provided.
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A simple decision tree will be constructed using R. A base-case analysis, as well as one-way deterministic and probabilistic analyses will be conducted.
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A Markov model will be designed using R. A Base case, as well as multi-way sensitivity analysis will be conducted.
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Results of both models will be presented in tabular and graphical form.
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Several examples of calibrating the Markov model to data to inform unknown/uncertain inputs will be shown.
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Advanced functions of R in decision modelling will be discussed. Examples include building microsimulation models or integrating network meta-analyses and decision models using R.
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Principles of good modelling practices using R (e.g. consistency, proper documentation etc) will be outlined.
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All R programming templates for decision modelling will be provided to participants after the course for future use along with a list of citations of the papers used in the examples and course.
