R and Shiny for Cost-Effectiveness Analyses: Why and When? A Hypothetical Case Study

Rose Hart (), Darren Burns, Bram Ramaekers, Shijie Ren, Daniel Gladwell, Will Sullivan, Niall Davison, Owain Saunders, Indeg Sly, Theresa Cain and Dawn Lee
Additional contact information
Rose Hart: BresMed Health Solutions
Darren Burns: BresMed Health Solutions
Bram Ramaekers: Maastricht University Medical Center
Shijie Ren: University of Sheffield
Daniel Gladwell: BresMed Health Solutions
Will Sullivan: BresMed Health Solutions
Niall Davison: BresMed Health Solutions
Owain Saunders: BresMed Health Solutions
Indeg Sly: BresMed Health Solutions
Theresa Cain: BresMed Health Solutions
Dawn Lee: BresMed Health Solutions

PharmacoEconomics, 2020, vol. 38, issue 7, No 9, 765-776

Abstract: Abstract Introduction Health economics models are typically built in Microsoft Excel® owing to its wide familiarity, accessibility and perceived transparency. However, given the increasingly rapid and analytically complex decision-making needs of both the pharmaceutical industry and the field of health economics and outcomes research (HEOR), the demands of cost-effectiveness analyses may be better met by the programming language R. Objective This case study provides an explicit comparison between Excel and R for contemporary cost-effectiveness analysis. Methods We constructed duplicate cost-effectiveness models using Excel and R (with a user interface built using the Shiny package) to address a hypothetical case study typical of contemporary health technology assessment. Results We compared R and Excel versions of the same model design to determine the advantages and limitations of the modelling platforms in terms of (i) analytical capability, (ii) data safety, (iii) building considerations, (iv) usability for technical and non-technical users and (v) model adaptability. Conclusions The findings of this explicit comparison are used to produce recommendations for when R might be more suitable than Excel in contemporary cost-effectiveness analyses. We conclude that selection of appropriate modelling software needs to consider case-by-case modelling requirements, particularly (i) intended audience, (ii) complexity of analysis, (iii) nature and frequency of updates and (iv) anticipated model run time.

Date: 2020
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DOI: 10.1007/s40273-020-00903-9

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