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dc.contributor.authorTrikalinos, Thomas
dc.contributor.authorBertsimas, Dimitris J
dc.contributor.authorSilberholz, John Michael
dc.date.accessioned2018-05-18T19:45:54Z
dc.date.available2018-05-18T19:45:54Z
dc.date.issued2016-09
dc.identifier.issn1386-9620
dc.identifier.issn1572-9389
dc.identifier.urihttp://hdl.handle.net/1721.1/115512
dc.description.abstractImportant decisions related to human health, such as screening strategies for cancer, need to be made without a satisfactory understanding of the underlying biological and other processes. Rather, they are often informed by mathematical models that approximate reality. Often multiple models have been made to study the same phenomenon, which may lead to conflicting decisions. It is natural to seek a decision making process that identifies decisions that all models find to be effective, and we propose such a framework in this work. We apply the framework in prostate cancer screening to identify prostate-specific antigen (PSA)-based strategies that perform well under all considered models. We use heuristic search to identify strategies that trade off between optimizing the average across all models’ assessments and being “conservative” by optimizing the most pessimistic model assessment. We identified three recently published mathematical models that can estimate quality-adjusted life expectancy (QALE) of PSA-based screening strategies and identified 64 strategies that trade off between maximizing the average and the most pessimistic model assessments. All prescribe PSA thresholds that increase with age, and 57 involve biennial screening. Strategies with higher assessments with the pessimistic model start screening later, stop screening earlier, and use higher PSA thresholds at earlier ages. The 64 strategies outperform 22 previously published expert-generated strategies. The 41 most “conservative” ones remained better than no screening with all models in extensive sensitivity analyses. We augment current comparative modeling approaches by identifying strategies that perform well under all models, for various degrees of decision makers’ conservativeness. Keywords: Comparative modeling, Decision analysis, Sensitivity analysis, Model averaging, Optimization, Prostate cancer screening, Simulation modelingen_US
dc.publisherSpringer USen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10729-016-9381-3en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceSpringer USen_US
dc.titleOptimal healthcare decision making under multiple mathematical models: application in prostate cancer screeningen_US
dc.typeArticleen_US
dc.identifier.citationBertsimas, Dimitris, et al. “Optimal Healthcare Decision Making under Multiple Mathematical Models: Application in Prostate Cancer Screening.” Health Care Management Science, vol. 21, no. 1, Mar. 2018, pp. 105–18.en_US
dc.contributor.departmentSloan School of Managementen_US
dc.contributor.mitauthorBertsimas, Dimitris J
dc.contributor.mitauthorSilberholz, John Michael
dc.relation.journalHealth Care Management Scienceen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-01-26T07:00:09Z
dc.language.rfc3066en
dc.rights.holderSpringer Science+Business Media New York
dspace.orderedauthorsBertsimas, Dimitris; Silberholz, John; Trikalinos, Thomasen_US
dspace.embargo.termsNen
dc.identifier.orcidhttps://orcid.org/0000-0002-1985-1003
dc.identifier.orcidhttps://orcid.org/0000-0003-0149-7955
mit.licenseOPEN_ACCESS_POLICYen_US


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