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An Overview of R in Health Decision Sciences

Author

Listed:
  • Hawre Jalal
  • Petros Pechlivanoglou
  • Eline Krijkamp
  • Fernando Alarid-Escudero
  • Eva Enns
  • M. G. Myriam Hunink

Abstract

As the complexity of health decision science applications increases, high-level programming languages are increasingly adopted for statistical analyses and numerical computations. These programming languages facilitate sophisticated modeling, model documentation, and analysis reproducibility. Among the high-level programming languages, the statistical programming framework R is gaining increased recognition. R is freely available, cross-platform compatible, and open source. A large community of users who have generated an extensive collection of well-documented packages and functions supports it. These functions facilitate applications of health decision science methodology as well as the visualization and communication of results. Although R’s popularity is increasing among health decision scientists, methodological extensions of R in the field of decision analysis remain isolated. The purpose of this article is to provide an overview of existing R functionality that is applicable to the various stages of decision analysis, including model design, input parameter estimation, and analysis of model outputs.

Suggested Citation

  • Hawre Jalal & Petros Pechlivanoglou & Eline Krijkamp & Fernando Alarid-Escudero & Eva Enns & M. G. Myriam Hunink, 2017. "An Overview of R in Health Decision Sciences," Medical Decision Making, , vol. 37(7), pages 735-746, October.
    • Handle: RePEc:sae:medema:v:37:y:2017:i:7:p:735-746
    DOI: 10.1177/0272989X16686559
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    References listed on IDEAS

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    1. Steven M. Shechter & Matthew D. Bailey & Andrew J. Schaefer & Mark S. Roberts, 2008. "The Optimal Time to Initiate HIV Therapy Under Ordered Health States," Operations Research, INFORMS, vol. 56(1), pages 20-33, February.
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    1. Isaac Corro Ramos & Talitha Feenstra & Salah Ghabri & Maiwenn Al, 2024. "Evaluating the Validation Process: Embracing Complexity and Transparency in Health Economic Modelling," PharmacoEconomics, Springer, vol. 42(7), pages 715-719, July.
    2. Fernando Alarid-Escudero & Eline Krijkamp & Eva A. Enns & Alan Yang & M. G. Myriam Hunink & Petros Pechlivanoglou & Hawre Jalal, 2023. "A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example," Medical Decision Making, , vol. 43(1), pages 21-41, January.
    3. Chase Hollman & Mike Paulden & Petros Pechlivanoglou & Christopher McCabe, 2017. "A Comparison of Four Software Programs for Implementing Decision Analytic Cost-Effectiveness Models," PharmacoEconomics, Springer, vol. 35(8), pages 817-830, August.
    4. Ke Gong & Ting Xie & Yong Luo & Hui Guo & Jinlan Chen & Zhiping Tan & Yifeng Yang & Li Xie, 2021. "Comprehensive analysis of lncRNA biomarkers in kidney renal clear cell carcinoma by lncRNA-mediated ceRNA network," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-24, June.
    5. Fernando Alarid-Escudero & Eline Krijkamp & Eva A. Enns & Alan Yang & M. G. Myriam Hunink & Petros Pechlivanoglou & Hawre Jalal, 2023. "An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example," Medical Decision Making, , vol. 43(1), pages 3-20, January.

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