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©The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved.
Decision modelling for economic evaluation of liver transplantation
Zhi Qu, Christian Krauth, Volker Eric Amelung, Alexander Kaltenborn, Jill Gwiasda, Lena Harries, Jan Beneke, Harald Schrem, Sebastian Liersch
Zhi Qu, Christian Krauth, Volker Eric Amelung, Alexander Kaltenborn, Jill Gwiasda, Lena Harries, Jan Beneke, Harald Schrem, Sebastian Liersch, Core Facility Quality Management and Health Technology Assessment in Transplantation, Integrated Research and Treatment Facility Transplantation (IFB-Tx), Hannover Medical School, Hannover 30625, Germany
Zhi Qu, Christian Krauth, Volker Eric Amelung, Lena Harries, Sebastian Liersch, Institute for Epidemiology, Social Medicine and Health Systems Research, Hannover Medical School, Hannover 30625, Germany
Harald Schrem, General, Visceral and Transplant Surgery, Hannover Medical School, Hannover 30625, Germany
Author contributions: Qu Z, Krauth C and Schrem H conceptualized the overview. Qu Z, Gwiasda J and Liersch S drafted the manuscript. Amelung VE, Kaltenborn A, Harries L, Beneke J and Schrem H critically reviewed the manuscript and contributed important intellectual contents.
Supported by a grant from the German Federal Ministry of Education and Research, No. 01EO1302.
Conflict-of-interest statement: The authors of this manuscript declare no conflicts of interest.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Zhi Qu, PhD, Doctor, Core Facility Quality Management and Health Technology Assessment in Transplantation, Integrated Research and Treatment Facility Transplantation (IFB-Tx), Hannover Medical School, Carl-Neuberg-Str. 1, Hannover 30625, Germany. qu.zhi@mh-hannover.de
Telephone: +49-511-5324453 Fax: +49-511-5325347
Received: May 25, 2018
Peer-review started: May 25, 2018
First decision: June 13, 2018
Revised: July 2, 2018
Accepted: October 9, 2018
Article in press: October 9, 2018
Published online: November 27, 2018
As the gap between a shortage of organs and the immense demand for liver grafts persists, every available donor liver needs to be optimized for utility, urgency and equity. To overcome this challenge, decision modelling might allow us to gather evidence from previous studies as well as compare the costs and consequences of alternative options. For public health policy and clinical intervention assessment, it is a potentially powerful tool. The most commonly used types of decision analytical models include decision trees, the Markov model, microsimulation, discrete event simulation and the system dynamic model. Analytic models could support decision makers in the field of liver transplantation when facing specific problems by synthesizing evidence, comprising all relevant options, generalizing results to other contexts, extending the time horizon and exploring the uncertainty. For modeling studies of economic evaluation for transplantation, understanding the current nature of the disease is crucial, as well as the selection of appropriate modelling techniques. The quality and availability of data is another key element for the selection and development of decision analytical models. In addition, good practice guidelines should be complied, which is important for standardization and comparability between economic outputs.
Core tip: This overview focuses on providing an understanding of decision modelling approaches and their application to liver transplantation, outlining the major characteristics of decision analytic models as well as the individual strengths and weaknesses of several main techniques for modelling. We believe decision modelling may be able to provide tools by bringing all evidence from other studies together and comparing the costs and consequences of alternative options to reach a decision. It is a particularly powerful tool for public health policy and clinical intervention assessment.
