Jalal H, Pechlivanoglou P, Krijkamp E, Alarid-Escudero F, Enns EA, Hunink, MGM. An Overview of R in Health Decision Sciences. Medical Decision Making, 2017;37(7):735-746.
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@article{drake2017kinked,
title={A kinked health insurance market: Employer-sponsored insurance under the cadillac tax},
author={Drake, Coleman and Higuera, Lucas and Alarid-Escudero, Fernando and Feldman, Roger},
journal={American Journal of Health Economics},
volume={3},
number={4},
pages={455--476},
year={2017},
publisher={MIT Press One Rogers St., Cambridge, MA 02142-1209 USA journals-info@ mit. edu}
}
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%0 Journal Article
%T An overview of R in health decision sciences
%A Jalal, Hawre
%A Pechlivanoglou, Petros
%A Krijkamp, Eline
%A Alarid-Escudero, Fernando
%A Enns, Eva
%A Hunink, MG Myriam
%J Medical decision making
%V 37
%N 7
%P 735-746
%@ 0272-989X
%D 2017
%I Sage Publications Sage CA: Los Angeles, CA
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TY - JOUR
T1 - An overview of R in health decision sciences
A1 - Jalal, Hawre
A1 - Pechlivanoglou, Petros
A1 - Krijkamp, Eline
A1 - Alarid-Escudero, Fernando
A1 - Enns, Eva
A1 - Hunink, MG Myriam
JO - Medical decision making
VL - 37
IS - 7
SP - 735
EP - 746
SN - 0272-989X
Y1 - 2017
PB - Sage Publications Sage CA: Los Angeles, CA
ER -
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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.