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Applied Cost-Effectiveness Modeling with R

LEVEL - Intermediate
TRACK - Methodological & Statistical Research
LENGTH: 4 Hours | Course runs 2 consecutive days, 2 hours each day

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Wednesday, 11 August 2021 | Course runs 2 consecutive days, 2 hours per day
7:00 AM-9:00AM Pacific Daylight Time (PDT)
10:00 AM-12:00PM Eastern Daylight Time (EDT)
2:00PM-4:00PM UTC/GMT Time
3:00PM-5:00PM British Standard Time (BST)
4:00PM-6:00PM Central European Standard Time (CEST)

Thursday, 12 August 2021 | Course runs 2 consecutive days, 2 hours per day
7:00 AM-9:00AM Pacific Daylight Time (PDT)
10:00 AM-12:00PM Eastern Daylight Time (EDT)
2:00PM-4:00PM UTC/GMT Time
3:00PM-5:00PM British Standard Time (BST)
4:00PM-6:00PM Central European Standard Time (CEST)

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DESCRIPTION

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). But more recently there has been increasing interest in using R and other programming languages for cost-effectiveness analysis which can offer advantages regarding the integration of input parameter estimation and model simulation, the evaluation of structural uncertainty, and the quantification of decision uncertainty, among others.1-2 Programming languages such as R also facilitate reproducibility of model-based cost-effectiveness analysis which is more relevant than ever given recent calls for increased transparancy.3-5 Yet, these tools are still relatively new, but there is an increased interest in learning opportunities, including in the ISPOR community (as demonstrated by attendance at the workshops on R and decision modeling at ISPOR Barcelona 2018 and ISPOR New Orleans 2019). Furthermore, R-based economic modeling tutorials have recently been published.6-7

In this short course, participants will learn how to use R to develop a number of different types of economic models to perform cost-effectiveness analysis. Economic models will include time-homogeneous and time-inhomogeneous Markov cohort models, partitioned survival models, and semi-Markov individual patient simulations. The underlying assumptions of each model type will be summarized and the implementation in R will be presented in an accessible manner. Participants will be asked 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). To make this interactive aspect of the course as efficient as possible, all participants will have access to the GitHub repository prior to the course. It will contain R code to run the economic models; R Markdown files to explain and reproduce the analyses covered in the course; and an illustrative R Shiny web application for one of the economic models to illustrate how R code can be used to create a web-based user interface.

FACULTY MEMBERS

Jeroen P. Jansen, PhD 
Chief Scientist
PRECISIONheor
Oakland, CA, USA
and
Associate Professor
Clinical Pharmacy, University of California
San Francisco, CA, USA

Devin Incerti

Basic Schedule:
2 hours of content daily