{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T22:48:34Z","timestamp":1780354114589,"version":"3.54.1"},"reference-count":26,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T00:00:00Z","timestamp":1561680000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"DEXHELPP is in the frame of COMET-Competence Centers for Excellent Technologies. DEXHELPP is supported by BMVIT, BMWFW and the state Vienna. The COMET program is transacted by the FFG.","award":["Project-Nr. 843550"],"award-info":[{"award-number":["Project-Nr. 843550"]}]},{"name":"Erasmus Mundus Western Balkans (ERAWEB), a project funded by the European Commission."}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Med Decis Making"],"published-print":{"date-parts":[[2019,7]]},"abstract":"<jats:p>Background. In state-transition models (STMs), decision problems are conceptualized using health states and transitions among those health states after predefined time cycles. The naive, commonly applied method (C) for cycle length conversion transforms all transition probabilities separately. In STMs with more than 2 health states, this method is not accurate. Therefore, we aim to describe and compare the performance of method C with that of alternative matrix transformation methods. Design. We compare 2 alternative matrix transformation methods (Eigenvalue method [E], Schure-Pad\u00e9 method [SP]) to method C applied in an STM of 3 different treatment strategies for women with breast cancer. We convert the given annual transition matrix into a monthly-cycle matrix and evaluate induced transformation errors for the transition matrices and the long-term outcomes: life years, quality-adjusted life-years, costs and incremental cost-effectiveness ratios, and the performance related to the decisions. In addition, we applied these transformation methods to randomly generated annual transition matrices with 4, 7, 10, and 20 health states. Results. In theory, there is no generally applicable correct transformation method. Based on our simulations, SP resulted in the smallest transformation-induced discrepancies for generated annual transition matrices for 2 treatment strategies. E showed slightly smaller discrepancies than SP in the strategy, where one of the direct transitions between health states was excluded. For long-term outcomes, the largest discrepancy occurred for estimated costs applying method C. For higher dimensional models, E performs best. Conclusions. In our modeling examples, matrix transformations (E, SP) perform better than transforming all transition probabilities separately (C). Transition probabilities based on alternative conversion methods should therefore be applied in sensitivity analyses.<\/jats:p>","DOI":"10.1177\/0272989x19851095","type":"journal-article","created":{"date-parts":[[2019,6,29]],"date-time":"2019-06-29T02:48:51Z","timestamp":1561776531000},"page":"509-522","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Alternative Conversion Methods for Transition Probabilities in State-Transition Models: Validity and Impact on Comparative Effectiveness and Cost-Effectiveness"],"prefix":"10.1177","volume":"39","author":[{"given":"Beate","family":"Jahn","sequence":"first","affiliation":[{"name":"Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT\u2014University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Kurzthaler","sequence":"additional","affiliation":[{"name":"Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT\u2014University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria"},{"name":"Institut f\u00fcr Theoretische Physik, Universit\u00e4t Innsbruck, Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jagpreet","family":"Chhatwal","sequence":"additional","affiliation":[{"name":"Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elamin H.","family":"Elbasha","sequence":"additional","affiliation":[{"name":"Merck & Co., Inc., Kenilworth, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annette","family":"Conrads-Frank","sequence":"additional","affiliation":[{"name":"Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT\u2014University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ursula","family":"Rochau","sequence":"additional","affiliation":[{"name":"Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT\u2014University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaby","family":"Sroczynski","sequence":"additional","affiliation":[{"name":"Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT\u2014University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Urach","sequence":"additional","affiliation":[{"name":"dwh GmbH\u2014Simulation Services and Technical Solutions, Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marvin","family":"Bundo","sequence":"additional","affiliation":[{"name":"Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT\u2014University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niki","family":"Popper","sequence":"additional","affiliation":[{"name":"dwh GmbH\u2014Simulation Services and Technical Solutions, Vienna, Austria"},{"name":"Institute for Analysis and Scientific Computing, Technical University, Vienna, Austria"},{"name":"DEXHELPP\u2014Decision Support for Health Policy and Planning, Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6425-7671","authenticated-orcid":false,"given":"Uwe","family":"Siebert","sequence":"additional","affiliation":[{"name":"Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT\u2014University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria"},{"name":"Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA"},{"name":"Center for Health Decision Science, Department of Health Policy and Management, Harvard T. 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