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Cost-effectiveness analysis with unordered decisions

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dc.contributor.authorDíez Vegas, Francisco Javier
dc.contributor.authorLuque Gallego, Manuel
dc.contributor.authorArias Calleja, Manuel
dc.contributor.authorPérez Martín, Jorge
dc.date.accessioned2024-05-20T11:43:08Z
dc.date.available2024-05-20T11:43:08Z
dc.date.issued2021-07
dc.description.abstractIntroduction Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Decision trees, the standard decision modeling technique for non-temporal domains, can only perform CEAs for very small problems. Influence diagrams can model much larger problems, but only when the decisions are totally ordered. Objective To develop a CEA method for problems with unordered or partially ordered decisions, such as finding the optimal sequence of tests for diagnosing a disease. Methods We explain how to model those problems using decision analysis networks (DANs), a new type of probabilistic graphical model, somewhat similar to Bayesian networks and influence diagrams. We present an algorithm for evaluating DANs with two criteria, cost and effectiveness, and perform some experiments to study its computational efficiency. We illustrate the representation framework and the algorithm using a hypothetical example involving two therapies and several tests and then present a DAN for a real-world problem, the mediastinal staging of non-small cell lung cancer. Results The evaluation of a DAN with two criteria, cost and effectiveness, returns a set of intervals for the willingness to pay, separated by incremental cost-effectiveness ratios (ICERs). The cost, the effectiveness, and the optimal intervention are specific for each interval, i.e., they depend on the willingness to pay. Conclusion Problems involving several unordered decisions can be modeled with DANs and evaluated in a reasonable amount of time. OpenMarkov, an open-source software tool developed by our research group, can be used to build the models and evaluate them using a graphical user interface.en
dc.description.versionversión final
dc.identifier.doi10.1016/j.artmed.2021.102064
dc.identifier.issn1873-2860
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12462
dc.journal.titleArtificial Intelligence in Medicine
dc.journal.volume117
dc.language.isoen
dc.publisherElsevier
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subject.keywordsCost-effectiveness analysis
dc.subject.keywordsDecision trees
dc.subject.keywordsProbabilistic graphical models
dc.subject.keywordsInfluence diagrams
dc.subject.keywordsDecision analysis networks
dc.titleCost-effectiveness analysis with unordered decisionses
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
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relation.isAuthorOfPublicatione94799f0-bdd0-45be-b6b2-426269f6ee46
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relation.isAuthorOfPublication.latestForDiscoveryc6032e20-a1d0-49b9-92e3-5c9f624ab143
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