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dc.contributor.authorStandfield, Lachlan
dc.contributor.authorComans, Tracy
dc.contributor.authorScuffham, Paul
dc.date.accessioned2017-05-03T16:09:11Z
dc.date.available2017-05-03T16:09:11Z
dc.date.issued2014
dc.identifier.issn0266-4623
dc.identifier.doi10.1017/S0266462314000117
dc.identifier.urihttp://hdl.handle.net/10072/62358
dc.description.abstractObjectives: The aim of this study was to assess if the use of Markov modeling (MM) or discrete event simulation (DES) for cost-effectiveness analysis (CEA) may alter healthcare resource allocation decisions. Methods: A systematic literature search and review of empirical and non-empirical studies comparing MM and DES techniques used in the CEA of healthcare technologies was conducted. Results: Twenty-two pertinent publications were identified. Two publications compared MM and DES models empirically, one presented a conceptual DES and MM, two described a DES consensus guideline, and seventeen drew comparisons between MM and DES through the authors' experience. The primary advantages described for DES over MM were the ability to model queuing for limited resources, capture individual patient histories, accommodate complexity and uncertainty, represent time flexibly, model competing risks, and accommodate multiple events simultaneously. The disadvantages of DES over MM were the potential for model overspecification, increased data requirements, specialized expensive software, and increased model development, validation, and computational time. Conclusions: Where individual patient history is an important driver of future events an individual patient simulation technique like DES may be preferred over MM. Where supply shortages, subsequent queuing, and diversion of patients through other pathways in the healthcare system are likely to be drivers of cost-effectiveness, DES modeling methods may provide decision makers with more accurate information on which to base resource allocation decisions. Where these are not major features of the cost-effectiveness question, MM remains an efficient, easily validated, parsimonious, and accurate method of determining the cost-effectiveness of new healthcare interventions.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent88059 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherCambridge University Press
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofpagefrom165
dc.relation.ispartofpageto172
dc.relation.ispartofissue2
dc.relation.ispartofjournalInternational Journal of Technology Assessment in Health Care
dc.relation.ispartofvolume30
dc.rights.retentionN
dc.subject.fieldofresearchHealth Economics
dc.subject.fieldofresearchPublic Health and Health Services
dc.subject.fieldofresearchApplied Economics
dc.subject.fieldofresearchcode140208
dc.subject.fieldofresearchcode1117
dc.subject.fieldofresearchcode1402
dc.titleMarkov modeling and discrete event simulation in health care: a systematic comparison
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2014 Cambridge University Press. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
gro.date.issued2015-08-06T00:11:59Z
gro.hasfulltextFull Text
gro.griffith.authorScuffham, Paul A.
gro.griffith.authorComans, Tracy
gro.griffith.authorStandfield, Lachlan B.


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