title: Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues.
abstract: For optimal solutions in health care, decision makers inevitably must evaluate trade-offs, which call for multi-attribute valuation methods. Researchers have proposed using best-worst scaling (BWS) methods which seek to extract information from respondents by asking them to identify the best and worst items in each choice set. While a companion paper describes the different types of BWS, application and their advantages and downsides, this contribution expounds their relationships with microeconomic theory, which also have implications for statistical inference. This article devotes to the microeconomic foundations of preference measurement, also addressing issues such as scale invariance and scale heterogeneity. Furthermore the paper discusses the basics of preference measurement using rating, ranking and stated choice data in the light of the findings of the preceding section. Moreover the paper gives an introduction to the use of stated choice data and juxtaposes BWS with the microeconomic foundations.
authors.affiliation: Institute Health Economics and Health Care Management, Hochschule Neubrandenburg, Neubrandenburg, Germany. muehlbacher@hs-nb.de.
authors.forename: Axel C
authors.initials: AC
authors.lastname: Mühlbacher
authors.affiliation: Department of Economics, University of Zürich, Zürich, Switzerland. peter.zweifel@econ.uzh.ch.
authors.forename: Peter
authors.initials: P
authors.lastname: Zweifel
authors.affiliation: Institute Health Economics and Health Care Management, Hochschule Neubrandenburg, Neubrandenburg, Germany. kaczynski@hs-nb.de.
authors.forename: Anika
authors.initials: A
authors.lastname: Kaczynski
authors.affiliation: Center for Clinical and Genetic Economics, Duke Clinical Research Institute, Duke University, Durham, USA. reed.johnson@duke.edu.
authors.forename: F Reed
authors.initials: FR
authors.lastname: Johnson
keywords: Best-Worst Scaling; Choice Experiments; Discrete Choice Experiments; MaxDiff Scaling; Stated Preferences
references.citation: Eur J Health Econ. 2011 Feb;12(1):87-95
references.pmid: 20446014
references.citation: Soc Sci Med. 2012 Jul;75(1):87-97
references.pmid: 22503838
references.citation: Pharmacoeconomics. 2016 Mar;34(3):273-84
references.pmid: 26589411
references.citation: Patient. 2010 Dec 1;3(4):275-83
references.pmid: 22273434
references.citation: BMJ. 2000 Jun 3;320(7248):1530-3
references.pmid: 10834905
references.citation: J Clin Epidemiol. 2007 Jan;60(1):34-42
references.pmid: 17161752
references.citation: Health Econ. 2004 Sep;13(9):901-7
references.pmid: 15362181
references.citation: Expert Rev Pharmacoecon Outcomes Res. 2010 Jun;10(3):259-67
references.pmid: 20545591
references.citation: J Health Econ. 2007 Jan;26(1):171-89
references.pmid: 16707175
references.citation: BMJ. 2004 Feb 14;328(7436):360-1
references.pmid: 14962852
references.citation: Health Econ. 2011 Mar;20(3):323-30
references.pmid: 20217834
references.citation: Health Econ Rev. 2016 Dec;6(1):2
references.pmid: 26743636
references.citation: Soc Sci Med. 2013 Jan;76(1):74-82
references.pmid: 23159304
references.citation: Value Health. 2013 Jan-Feb;16(1):3-13
references.pmid: 23337210
references.citation: Health Econ. 2012 Feb;21(2):145-72
references.pmid: 22223558
references.citation: Patient. 2010 Dec 1;3(4):249-56
references.pmid: 22273432
references.citation: Health Econ. 2012 Jun;21(6):730-41
references.pmid: 21557381