Abstract
This tutorial provides a step-by-step guide to performing cost-effectiveness analysis using a multi-state modeling approach. Alongside the tutorial, we provide easy-to-use functions in the statistics package R. We argue that this multi-state modeling approach using a package such as R has advantages over approaches where models are built in a spreadsheet package. In particular, using a syntax-based approach means there is a written record of what was done and the calculations are transparent. Reproducing the analysis is straightforward as the syntax just needs to be run again. The approach can be thought of as an alternative way to build a Markov decision-analytic model, which also has the option to use a state-arrival extended approach. In the state-arrival extended multi-state model, a covariate that represents patients’ history is included, allowing the Markov property to be tested. We illustrate the building of multi-state survival models, making predictions from the models and assessing fits. We then proceed to perform a cost-effectiveness analysis, including deterministic and probabilistic sensitivity analyses. Finally, we show how to create 2 common methods of visualizing the results—namely, cost-effectiveness planes and cost-effectiveness acceptability curves. The analysis is implemented entirely within R. It is based on adaptions to functions in the existing R package mstate to accommodate parametric multi-state modeling that facilitates extrapolation of survival curves.
Highlights
Supplementary material for this article is available on the Medical Decision Making Web site at http://journals.sagepub.com/home/mdm
This article demonstrates how a cost-effectiveness analysis can be carried out within a multi-state modeling survival analysis framework using the statistical software R,6 which is freely available under the GNU General Public Licence
The significance, statistically and clinically, of the covariate can help in deciding whether the Markov assumption is reasonable and the approach to take for the analysis. The aims of this tutorial article are 1) to introduce the ‘‘state-arrival extended’’ multi-state model as a tool to test the Markov property and 2) to provide a step-by-step guide to how multi-state modeling can be used for carrying out a cost-effectiveness analysis, including discounting of costs/benefits and deterministic and probabilistic sensitivity analyses
Summary
The data used in this article are based on a trial comparing rituximab in combination with fludarabine and cyclophosphamide (RFC) v. fludarabine and cyclophosphamide alone (FC) for the first-line treatment of chronic lymphocytic leukemia (CLL8).[17]. The data used in this article are based on a trial comparing rituximab in combination with fludarabine and cyclophosphamide (RFC) v. There were 106 progressions, 23 deaths after progression, and 21 deaths without progression among those in the RFC arm. Patients were in the trial for up to 4 years, and not all were observed to the end of their lives. This meant extrapolation of survival was necessary to obtain a representation of the whole duration of life since entry into the trial. The published Kaplan-Meier curves in the manufacturer’s report[19] were digitized using Enguage[20] to generate the data for the analysis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Medical decision making : an international journal of the Society for Medical Decision Making
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
