A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling.
The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research...
| Publicado en: | PharmacoEconomics Vol. 37; no. 11; pp. 1329 - 1340 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2019
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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=139654914&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139654914 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11707690 C5G jtl: PharmacoEconomics issn: 11707690 maglogo: N pubinfo: dt: Nov2019 vid: 37 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139654914 139654914 NLM31549359 139654914 10.1007/s40273-019-00837-x NLM31549359 139654914 ppf: 1329 ppct: 11 formats: tig: atl: A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling. aug: au: Alarid-Escudero, Fernando Krijkamp, Eline M. Pechlivanoglou, Petros Jalal, Hawre Kao, Szu-Yu Zoe Yang, Alan Enns, Eva A. affil: Drug Policy Program, Center for Research and Teaching in Economics (CIDE)-CONACyT, Circuito Tecnopolo Norte 117, Col. Tecnopolo Pocitos II, 20313, Aguascalientes, AGS, Mexico sug: subj: Decision Making Software Decision Support Techniques Reproducibility of Results Cost Benefit Analysis Funding Source ab: The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modelers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, we propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R. The proposed framework consists of a conceptual, modular structure and coding recommendations for the implementation of model-based decision analyses in R. This framework defines a set of common decision model elements divided into five components: (1) model inputs, (2) decision model implementation, (3) model calibration, (4) model validation, and (5) analysis. The first four components form the model development phase. The analysis component is the application of the fully developed decision model to answer the policy or the research question of interest, assess decision uncertainty, and/or to determine the value of future research through value of information (VOI) analysis. In this framework, we also make recommendations for good coding practices specific to decision modeling, such as file organization and variable naming conventions. We showcase the framework through a fully functional, testbed decision model, which is hosted on GitHub for free download and easy adaptation to other applications. The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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