Decision-analytic modeling studies: An overview for clinicians using multiple myeloma as an example

U Rochau; B Jahn; V Qerimi; E A Burger; C Kurzthaler; M Kluibenschaedl; E Willenbacher; G Gastl; W Willenbacher; U Siebert

doi:10.1016/j.critrevonc.2014.12.017

Decision-analytic modeling studies: An overview for clinicians using multiple myeloma as an example

Crit Rev Oncol Hematol. 2015 May;94(2):164-78. doi: 10.1016/j.critrevonc.2014.12.017. Epub 2014 Dec 31.

Authors

U Rochau¹, B Jahn², V Qerimi³, E A Burger⁴, C Kurzthaler⁵, M Kluibenschaedl⁶, E Willenbacher⁷, G Gastl⁸, W Willenbacher⁹, U Siebert¹⁰

Affiliations

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.

PMID: 25620327
DOI: 10.1016/j.critrevonc.2014.12.017

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.

Keywords: Cost–effectiveness analysis; Decision-analytic modeling; Health economic modeling; Multiple myeloma; Systematic overview.

Publication types

Meta-Analysis
Research Support, Non-U.S. Gov't
Review

MeSH terms

Computer Simulation
Cost-Benefit Analysis
Decision Making*
Decision Support Techniques*
Disease Management
Humans
Models, Statistical
Multiple Myeloma / diagnosis
Multiple Myeloma / mortality
Multiple Myeloma / therapy
Survival Analysis