2020
DOI: 10.1007/s40273-020-00903-9
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R and Shiny for Cost-Effectiveness Analyses: Why and When? A Hypothetical Case Study

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 cont… Show more

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Cited by 11 publications
(8 citation statements)
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“…According to Pillkahn (2008), ‘scenarios are hypothetical illustrations of the future that describe a cross-section in an established context, describe development paths and serve as a guide’. Hart et al (2020) used a hypothetical case study to compare various cost-effectiveness analyses. Stojanović et al (2014) focused on scenario techniques in urban planning to capitalize on their utility in times of uncertainty and complexity.…”
Section: Resultsmentioning
confidence: 99%
“…According to Pillkahn (2008), ‘scenarios are hypothetical illustrations of the future that describe a cross-section in an established context, describe development paths and serve as a guide’. Hart et al (2020) used a hypothetical case study to compare various cost-effectiveness analyses. Stojanović et al (2014) focused on scenario techniques in urban planning to capitalize on their utility in times of uncertainty and complexity.…”
Section: Resultsmentioning
confidence: 99%
“…We provided the R code on GitHub (https://github.com/YasuhiroHag iwara/GBTmapping) to generate the mapped EQ-5D-5L index from the EORTC QLQ-C30 based on GBT and regression approaches in health technology assessment and health economic evaluation. 21,22 The prediction error of the GBT-based direct mapping algorithms was the smallest among the evaluated mapping algorithms in the training data set, and this algorithm decreased overprediction in poor health and underprediction in good health compared with the regression-based mapping algorithms. This result reflects the flexibility of GBTs, but this flexibility induced overfitting; in the test data set, the prediction error measured by the RMSE and MAE was larger using the GBT-based direct mapping algorithms than the regression-based mapping algorithms.…”
Section: Discussionmentioning
confidence: 96%
“…The de novo model was developed in R Shiny (R Foundation for Statistical Computing, Vienna, Austria) 69 to leverage the benefits of using modern programming languages such as R (R Foundation for Statistical Computing, Vienna, Austria) 70 while providing an accessible interface through the Shiny package. To improve model transparency as well as model credibility and for consistency with suggested good practices and conventions, the technical implementation of the computational model was inspired by recent work of the Data Analytics Research and Technology in Healthcare Group 71,72 and others.…”
Section: Model Structurementioning
confidence: 99%