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Markov influence diagrams: a graphical tool for cost-effectiveness analysis

dc.contributor.authorYebra, Mar
dc.contributor.authorBermejo, Iñigo
dc.contributor.authorPalacios Alonso, Miguel Ángel
dc.contributor.authorArias Calleja, Manuel::virtual::3078::600
dc.contributor.authorLuque Gallego, Manuel::virtual::3079::600
dc.contributor.authorPérez Martín, Jorge::virtual::3080::600
dc.contributor.authorDíez Vegas, Francisco Javier::virtual::6771::600
dc.contributor.authorArias Calleja, Manuel
dc.contributor.authorLuque Gallego, Manuel
dc.contributor.authorPérez Martín, Jorge
dc.contributor.authorDíez Vegas, Francisco Javier
dc.contributor.authorArias Calleja, Manuel
dc.contributor.authorLuque Gallego, Manuel
dc.contributor.authorPérez Martín, Jorge
dc.contributor.authorDíez Vegas, Francisco Javier
dc.contributor.authorArias Calleja, Manuel
dc.contributor.authorLuque Gallego, Manuel
dc.contributor.authorPérez Martín, Jorge
dc.contributor.authorDíez Vegas, Francisco Javier
dc.date.accessioned2024-05-20T11:42:36Z
dc.date.available2024-05-20T11:42:36Z
dc.date.issued2017-01-11
dc.description.abstractMarkov influence diagrams (MIDs) are a new type of probabilistic graphical models that extend influence diagrams in the same way as Markov decision trees extend decision trees. They have been designed to build state-transition models, mainly in medicine, and perform cost-effectiveness analysis. Using a causal graph that may contain several variables per cycle, MIDs can model various features of the patient without multiplying the number of states; in particular, they can represent the history of the patient without using tunnel states. OpenMarkov, an open-source tool, allows the decision analyst to build and evaluate MIDs—including cost-effectiveness analysis and several types of deterministic and probabilistic sensitivity analysis—with a graphical user interface, without writing any code. This way, MIDs can be used to easily build and evaluate complex models whose implementation as spreadsheets or decision trees would be cumbersome or unfeasible in practice. Furthermore, many problems that previously required discrete event simulation can be solved with MIDs, i.e., within the paradigm of state-transition models, in which many health economists feel more comfortable.en
dc.description.versionversión final
dc.identifier.doihttp://doi.org/10.1177/0272989X16685088
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12450
dc.journal.titleMedical Decision Making
dc.journal.volume34
dc.language.isoen
dc.publisherSociety for Medical Decision Making
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsMarkov models
dc.subject.keywordsinfluence diagrams
dc.subject.keywordscost-effectiveness analysis
dc.subject.keywordsoutcomes research
dc.titleMarkov influence diagrams: a graphical tool for cost-effectiveness analysises
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
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