Health Economic Evaluation Using Markov Models in R for Microsoft Excel Users: A Tutorial

Nathan Green (), Felicity Lamrock (), Nichola Naylor, Jack Williams and Andrew Briggs
Additional contact information
Nathan Green: University College London
Felicity Lamrock: Queen’s University Belfast
Nichola Naylor: UK Health Security Agency
Jack Williams: London School of Hygiene & Tropical Medicine
Andrew Briggs: London School of Hygiene & Tropical Medicine

PharmacoEconomics, 2023, vol. 41, issue 1, No 2, 5-19

Abstract: Abstract A health economic evaluation (HEE) is a comparative analysis of alternative courses of action in terms of both costs and consequences. A cost-effectiveness analysis is a type of HEE that compares an intervention to one or more alternatives by estimating how much it costs to gain an additional unit of health outcome. Cost-effectiveness analyses are commonly performed using Microsoft (MS) Excel. However, there is current interest in using other software that is better suited to more complex problems, methods, and data, as well as improved reproducibility and transparency. That is, it is increasingly important to be able to repeat an analysis of a particular data set and obtain the same results, and access the analysis and results in a clear and comprehensive openly available form. In this tutorial we provide a step-by-step guide on how to implement a mainstay model of HEE, namely a Markov model, in the statistical programming language R. The adoption of R for the purpose of cost-effectiveness analysis is highly dependent on the ability of the health economic modeller to understand, learn, and apply programming-type skills. R is likely to be less familiar than MS Excel for many modellers and so coding a cost-effectiveness model in R can be a large jump. We describe the technical details from the perspective of a MS Excel user to help bridge the gap between software and reduce the learning curve by providing for the first-time side-by-side comparisons of the Markov model example in MS Excel and R.

Date: 2023
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DOI: 10.1007/s40273-022-01199-7

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