Introduction to Decision Modelling Using R

Introduction to Decision Modelling Using R is a short course aimed at researchers that want to learn how to implement simple decision trees and Markov models using R.

Background

Economic evaluations often rely on decision models. As decision modelling advances, more complex models are being designed to better represent the underlying clinical conditions. In addition, decision models increasingly rely on new statistical and mathematical techniques (e.g. model calibration, value of information, evidence synthesis). Current commercially available tools provide limited flexibility in embedding such complex statistical approaches within the decision model framework. R is an environment where statistical analysis on primary or literature data can be combined with decision modelling and the results can be presented in publication ready form. Hence, R can embed most components of decision modelling within a single framework. Among other benefits, this facilitates sensitivity analyses while accounting for model uncertainties. In addition, R can accommodate more complex model structures and improve computational times. Finally, the fact that R is freely available improves model transparency and reproducibility.

Course Type

Half day

Course Level

Intermediate

Format Requirements

The course will focus on how to implement simple decision trees and Markov models using R. Therefore a good understanding of decision modeling is necessary. The participants will be expected to have some experience with designing decision trees and Markov models and have basic understanding of statistics for economic evaluation. Finally, basic knowledge of R is required. Participants will need to bring their own laptops for practical exercises. R and RStudio will need to be pre-installed. Installation instructions will follow prior to the course.

Overview

This course offers participants the opportunity to be introduced to how to build decision models, such as decision trees and Markov cohort models, for economic evaluation using the R statistical software. R is an increasingly more popular platform where various components of an economic evaluation (person-level data analysis, evidence synthesis, decision modelling etc.) can be combined in a single framework.

Description & Objectives

The course will cover the basics of building decision models using R. First a very 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. Next, a simple decision tree will be constructed using R. A base-case analysis, as well as one way and probabilistic analyses will be conducted. Subsequently, a simple Markov model will be designed using R. Base case and multi-way sensitivity analysis will be conducted. Results of both models will be presented in tabular and graphical form. Principles of good modelling practices using R (e.g. consistency, proper documentation etc) will be outlined. Finally, advanced functions of R in decision modelling will be briefly discussed.

By the end of the course the participants will be able to:

• Build a simple decision tree in R
• Build a simple Markov model in R
• Assign distributions to model parameters and conduct probabilistic analysis in R
• Create and export tabular and graphical representations of the results of the decision model. *Have a broader understanding of the advantages associated with using R in decision modelling.

All R programming templates for decision modelling will be provided to participants after the course for future use.