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.
ToshJWailooA. Review of Software for Decision Modelling. Sheffield, UK: Decision Support Unit, University of Sheffield; 2008.
2.
WilliamsCLewseyJDBriggsAHMackayDF. Cost-effectiveness analysis in R using a multi-state modelling survival analysis framework: a tutorial. Med Decis Making. In press.
3.
HawkinsNSculpherMEpsteinD. Cost-effectiveness analysis of treatments for chronic disease: using R to incorporate time dependency of treatment response. Med Decis Making. 2005;25(5):511–9.
4.
EddyDMHollingworthWCaroJJet al. Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7. Med Decis Making. 2012;32(5):733–43.
5.
BriggsAHWeinsteinMCFenwickEAet al. Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6. Med Decis Making. 2012;32(5):722–32.
6.
PitmanRFismanDZaricGSet al. Dynamic transmission modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-5. Med Decis Making. 2012;32(5):712–21.
7.
KarnonJStahlJBrennanACaroJJMarJMollerJ. Modeling using discrete event simulation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-4. Med Decis Making. 2012;32(5):701–11.
8.
SiebertUAlagozOBayoumiAMet al. State-transition modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3. Med Decis Making. 2012;32(5):690–700.
9.
RobertsMRussellLBPaltielADet al. Conceptualizing a model: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-2. Med Decis Making. 2012;32(5):678–89.
10.
CaroJJBriggsAHSiebertUKuntzKM, Force I-SMGRPT. Modeling good research practices—overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-1. Med Decis Making. 2012;32(5):667–77.
11.
BeckJRPaukerSG. The Markov process in medical prognosis. Med Decis Making. 1983;3(4):419–58.
12.
IskandarRAlarid-EscuderoFKuntzKM. Does Size Matter? A Method for Adaptively Determining Cycle Length in a State-Transition Model. Paper presented at the 37th Annual Meeting of the Society for Medical Decision Making; St. Louis, MO; 18–21 October 2015.
13.
ShechterSMBaileyMDSchaeferAJRobertsMS. The optimal time to initiate HIV therapy under ordered health states. Oper Res. 2008;56(1):20–33.
14.
WilenskyU. NetLogo: Center for Connected Learning and Computer-Based Modeling, Evanston, IL: Northwestern University; 1999 Available from: URL: http://ccl.northwestern.edu/netlogo/.
15.
LatimerNR. Survival analysis for economic evaluations alongside clinical trials—extrapolation with patient-level data: inconsistencies, limitations, and a practical guide. Med Decis Making. 2013;33(6):743–54.
16.
WeltonNJSuttonAJCooperNAbramsKRAdesAE. Evidence Synthesis for Decision Making in Healthcare. New York: John Wiley & Sons; 2012.
17.
BurchardEGOhSSForemanMGCeledonJC. Moving toward true inclusion of racial/ethnic minorities in federally funded studies: a key step for achieving respiratory health equality in the United States. Am J Respir Crit Care Med. 2015;191(5):514–21.
18.
AdesAECaldwellDMRekenSWeltonNJSuttonAJDiasS. Evidence synthesis for decision making 7: a reviewer’s checklist. Med Decis Making. 2013;33(5):679–91.
19.
DiasSSuttonAJAdesAEWeltonNJ. Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med Decis Making. 2013;33(5):607–17.
20.
DiasSSuttonAJWeltonNJAdesAE. Evidence synthesis for decision making 6: embedding evidence synthesis in probabilistic cost-effectiveness analysis. Med Decis Making. 2013;33(5):671–8.
21.
DiasSSuttonAJWeltonNJAdesAE. Evidence synthesis for decision making 3: heterogeneity-subgroups, meta-regression, bias, and bias-adjustment. Med Decis Making. 2013;33(5):618–40.
22.
DiasSWeltonNJSuttonAJAdesAE. Evidence synthesis for decision making 5: the baseline natural history model. Med Decis Making. 2013;33(5):657–70.
23.
DiasSWeltonNJSuttonAJAdesAE. Evidence synthesis for decision making 1: introduction. Med Decis Making. 2013;33(5):597–606.
24.
DiasSWeltonNJSuttonAJCaldwellDMLuGAdesAE. Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials. Med Decis Making. 2013;33(5):641–56.
25.
ShkolnikovVMSobotkaT. Human Fertility Database: Max Planck Institute for Demographic Research (Germany) and Vienna Institute of Demography (Austria). Available from: URL: www.humanfertility.org.
26.
ShkolnikovVBarbieriMWilmothJ. The Human Mortaliy Database: University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). Available from: URL: http://www.mortality.org/.
27.
National Institutes of Health. Surveillance, Epidemiology, and End Results (SEER) program populations 1969-2014. Rockville, MD: National Cancer Institute, DCCPS, Surveillance Research Program, Surveillance Systems Branch.
28.
BoothHHyndmanRTickleLde JongP. Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions. Demographic Research. 2006;15(9):289–310.
29.
Goldhaber-FiebertJDBrandeauML. Modeling and calibration for exposure to time-varying, modifiable risk factors: the example of smoking behavior in India. Med Decis Making. 2015;35(2):196–210.
30.
TaylorDCPawarVKruzikasDTGilmoreKESanonMWeinsteinMC. Incorporating calibrated model parameters into sensitivity analyses: deterministic and probabilistic approaches. Pharmacoeconomics. 2012;30(2):119–26.
31.
ChiaYLSalzmanPPlevritisSKGlynnPW. Simulation-based parameter estimation for complex models: a breast cancer natural history modelling illustration. Stat Methods Med Res. 2004;13(6):507–24.
32.
KimJJKuntzKMStoutNKet al. Multiparameter calibration of a natural history model of cervical cancer. Am J Epidemiol. 2007;166(2):137–50.
33.
TaylorDCPawarVKruzikasDet al. Methods of model calibration: observations from a mathematical model of cervical cancer. Pharmacoeconomics. 2010;28(11):995–1000.
34.
StoutNKKnudsenABKongCYMcMahonPMGazelleGS. Calibration methods used in cancer simulation models and suggested reporting guidelines. Pharmacoeconomics. 2009;27(7):533–45.
35.
Goldhaber-FiebertJDJalalHJ. Some health states are better than others: using health state rank order to improve probabilistic analyses. Med Decis Making. 2016;36(8):927–40.
36.
StrongMOakleyJEBrennanABreezeP. Estimating the expected value of sample information using the probabilistic sensitivity analysis sample: a fast, nonparametric regression-based method. Med Decis Making. 2015;35(5):570–83.
37.
JalalHGoldhaber-FiebertJDKuntzKM. Computing expected value of partial sample information from probabilistic sensitivity analysis using linear regression metamodeling. Med Decis Making. 2015;35(5):584–95.
38.
BrennanAKharroubiSA. Efficient computation of partial expected value of sample information using Bayesian approximation. J Health Econ. 2007;26(1):122–48.
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