R in Health Decision Sciences

Introduction
Health decision sciences (HDS) is the field of study that involves improving the quality of decisions in the presence of uncertainty in the healthcare setting. HDS has the goal to allocate limited healthcare resources optimally. Since these health care decisions have substantial consequences it is important to communicate the uncertainties and trade-offs related to these decisions with all health-care professionals and (health) policy makers. HDS combines information from multiple disciplines, like epidemiology, health economics, biostatistics and computer science. HDS estimates the consequences of all the actions considered.

The most commonly used mathematical models in HDS are decision trees, Markov models , and microsimulation.

R is a programming language that has been mostly used for statistical programming across many academic disciplines such as statistics and engineering. R has gained significant popularity in Health Decision Sciences over the past decade. A key characteristic of R is its vast number of packages that can extend the functionality of the programming language.

Academic groups promoting the use of R in HDS
At the 37th Annual North American Meeting of the Society for Medical Decision Making (SMDM) in St. Louis, MO, USA, a group of researchers and students created the Decision Analysis in R for Technologies in Health (DARTH) workgroup. The group is an international endeavor comprised of members with shared interests and a significant experience modeling in R from the University of Minnesota, the University of Pittsburgh, The Hospital for Sick Children, University of Toronto, and Erasmus University of Rotterdam. As part of DARTH’s agenda, they develop tutorials on how to implement decision-analytic models using R.

Good coding practices

 * Google’s R style Guide
 * Hadley Wickham’s style guide

The workgroup on Github
DARTH's most up to date work can be found in the group's GitHub page.