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An Overview of R in Health Decision Sciences.

Authors
  • Jalal, Hawre1, 2, 3, 4, 5
  • Pechlivanoglou, Petros1, 2, 3, 4, 5
  • Krijkamp, Eline1, 2, 3, 4, 5
  • Alarid-Escudero, Fernando1, 2, 3, 4, 5
  • Enns, Eva1, 2, 3, 4, 5
  • Hunink, M G Myriam1, 2, 3, 4, 5
  • 1 University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA (HJ).
  • 2 The Hospital for Sick Children, Toronto and University of Toronto, Toronto, Ontario, Canada (PP). , (Canada)
  • 3 Erasmus MC, Rotterdam, the Netherlands (EK). , (Netherlands)
  • 4 University of Minnesota School of Public Health, Minneapolis, MN, USA (FA-E, EE).
  • 5 Erasmus MC, Rotterdam, The Netherlands and Harvard T.H. Chan School of Public Health, Boston, MA, USA (MGMH). , (Netherlands)
Type
Published Article
Journal
Medical decision making : an international journal of the Society for Medical Decision Making
Publication Date
Oct 01, 2017
Volume
37
Issue
7
Pages
735–746
Identifiers
DOI: 10.1177/0272989X16686559
PMID: 28061043
Source
Medline
Keywords
License
Unknown

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.

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