Cost-effectiveness analysis in R using a multi-state modelling survival analysis framework: a tutorial

Williams, C., Lewsey, J. D. , Briggs, A. H. and Mackay, D. F. (2017) Cost-effectiveness analysis in R using a multi-state modelling survival analysis framework: a tutorial. Medical Decision Making, 37(4), pp. 340-352. (doi:10.1177/0272989X16651869) (PMID:27281337) (PMCID:PMC5424858)

Williams, C., Lewsey, J. D. , Briggs, A. H. and Mackay, D. F. (2017) Cost-effectiveness analysis in R using a multi-state modelling survival analysis framework: a tutorial. Medical Decision Making, 37(4), pp. 340-352. (doi:10.1177/0272989X16651869) (PMID:27281337) (PMCID:PMC5424858)

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Abstract

This tutorial provides a step-by-step guide to performing cost-effectiveness analysis using a multi-state modelling approach. Alongside the tutorial we provide easy-to-use functions in the statistics package R. We argue this multi-state modelling 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 if the Markov property does not hold. 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 two common methods of visualising 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 modelling which facilitates extrapolation of survival curves.

Item Type:Articles
Additional Information:Financial support for this study was provided entirely by a PhD studentship from the Medical Research Council in the UK.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Lewsey, Professor James and Williams, Miss Claire and Briggs, Professor Andrew and Mackay, Dr Daniel
Authors: Williams, C., Lewsey, J. D., Briggs, A. H., and Mackay, D. F.
College/School:College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > Health Economics and Health Technology Assessment
Journal Name:Medical Decision Making
Publisher:Sage
ISSN:0272-989X
ISSN (Online):1552-681X
Published Online:08 June 2016
Copyright Holders:Copyright © 2016 The Authors
First Published:First published in Medical Decision Making 37(4): 340-352
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
580941MRC Doctoral Training Grant 2011-2015Mary Beth KneafseyMedical Research Council (MRC)MR/J50032X/1VICE PRINCIPAL RESEARCH & ENTERPRISE