Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial.
Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a...
| Publicado en: | Medical Decision Making Vol. 38; no. 3; pp. 400 - 423 |
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| Autores principales: | , , , , , |
| Formato: | algorithm tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Apr2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=128747345&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128747345 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0272989X DKI jtl: Medical Decision Making issn: 0272989X maglogo: Y pubinfo: dt: Apr2018 vid: 38 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 128747345 128747345 128747345 10.1177/0272989X18754513 128747345 ppf: 400 ppct: 23 formats: tig: atl: Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial. aug: au: Krijkamp, Eline M. Alarid-Escudero, Fernando Enns, Eva A. Jalal, Hawre J. Hunink, M. G. Myriam Pechlivanoglou, Petros affil: Erasmus MC, Epidemiology Department, Rotterdam, The Netherlands sug: subj: Decision Making, Clinical Computer Simulation Programming Languages Software ab: Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions. pubtype: Academic Journal doctype: algorithm tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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