Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication

DOI

10.1177/0272989x16651869

Type

Journal article

Journal

Medical Decision Making

Publisher

SAGE Publications

Publication Date

05/2017

Volume

37

Pages

340 - 352