U Rochau1, B Jahn2, V Qerimi3, E A Burger4, C Kurzthaler5, M Kluibenschaedl6, E Willenbacher7, G Gastl8, W Willenbacher9, U Siebert10. 1. Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Area 4 Health Technology Assessment and Bioinformatics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria. Electronic address: ursula.rochau@umit.at. 2. Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria. Electronic address: beate.jahn@umit.at. 3. Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Faculty of Pharmacy, School of PhD Studies, Ss. Cyril and Methodius University in Skopje, Macedonia. Electronic address: vjollca.qerimi@umit.at. 4. Department of Health Management and Health Economics, University of Oslo, Norway; Center for Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston, MA, USA. Electronic address: emily.burger@medisin.uio.no. 5. Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria. Electronic address: christina.kurzthaler@umit.at. 6. Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria. Electronic address: martina.kluibenschaedl@umit.at. 7. Area 4 Health Technology Assessment and Bioinformatics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria; Internal Medicine V, Hematology and Oncology, Medical University, Innsbruck, Austria. Electronic address: ella.willenbacher@i-med.ac.at. 8. Area 4 Health Technology Assessment and Bioinformatics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria; Internal Medicine V, Hematology and Oncology, Medical University, Innsbruck, Austria. Electronic address: guenther.gastl@i-med.ac.at. 9. Area 4 Health Technology Assessment and Bioinformatics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria; Internal Medicine V, Hematology and Oncology, Medical University, Innsbruck, Austria. Electronic address: wolfgang.willenbacher@uki.at. 10. Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Area 4 Health Technology Assessment and Bioinformatics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria; Center for Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston, MA, USA; Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: uwe.siebert@umit.at.
Abstract
PURPOSE: The purpose of this study was to provide a clinician-friendly overview of decision-analytic models evaluating different treatment strategies for multiple myeloma (MM). METHODS: We performed a systematic literature search to identify studies evaluating MM treatment strategies using mathematical decision-analytic models. We included studies that were published as full-text articles in English, and assessed relevant clinical endpoints, and summarized methodological characteristics (e.g., modeling approaches, simulation techniques, health outcomes, perspectives). RESULTS: Eleven decision-analytic modeling studies met our inclusion criteria. Five different modeling approaches were adopted: decision-tree modeling, Markov state-transition modeling, discrete event simulation, partitioned-survival analysis and area-under-the-curve modeling. Health outcomes included survival, number-needed-to-treat, life expectancy, and quality-adjusted life years. Evaluated treatment strategies included novel agent-based combination therapies, stem cell transplantation and supportive measures. CONCLUSION: Overall, our review provides a comprehensive summary of modeling studies assessing treatment of MM and highlights decision-analytic modeling as an important tool for health policy decision making.
PURPOSE: The purpose of this study was to provide a clinician-friendly overview of decision-analytic models evaluating different treatment strategies for multiple myeloma (MM). METHODS: We performed a systematic literature search to identify studies evaluating MM treatment strategies using mathematical decision-analytic models. We included studies that were published as full-text articles in English, and assessed relevant clinical endpoints, and summarized methodological characteristics (e.g., modeling approaches, simulation techniques, health outcomes, perspectives). RESULTS: Eleven decision-analytic modeling studies met our inclusion criteria. Five different modeling approaches were adopted: decision-tree modeling, Markov state-transition modeling, discrete event simulation, partitioned-survival analysis and area-under-the-curve modeling. Health outcomes included survival, number-needed-to-treat, life expectancy, and quality-adjusted life years. Evaluated treatment strategies included novel agent-based combination therapies, stem cell transplantation and supportive measures. CONCLUSION: Overall, our review provides a comprehensive summary of modeling studies assessing treatment of MM and highlights decision-analytic modeling as an important tool for health policy decision making.
Authors: Anton Safonov; Shiyi Wang; Cary P Gross; Divyansh Agarwal; Giampaolo Bianchini; Lajos Pusztai; Christos Hatzis Journal: Breast Cancer Res Treat Date: 2016-01-09 Impact factor: 4.872