Related Experiment Video
Updated: Dec 25, 2025

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
Published on: September 10, 2018
R and Shiny for Cost-Effectiveness Analyses: Why and When? A Hypothetical Case Study
Rose Hart1, Darren Burns2, Bram Ramaekers3
1BresMed Health Solutions, Sheffield, UK. rhart@bresmed.com.
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.
More Related Videos
Related Concept Videos
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Introduction to R
Study Design in Statistics
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Bioequivalence Data: Statistical Interpretation
Study Designs in Epidemiology
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Pharmacokinetic Models: Comparison and Selection Criterion
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

