title: Using blocked fractional factorial designs to construct discrete choice experiments for healthcare studies.
abstract: Discrete choice experiments (DCEs) are increasingly used for studying and quantifying subjects preferences in a wide variety of healthcare applications. They provide a rich source of data to assess real-life decision-making processes, which involve trade-offs between desirable characteristics pertaining to health and healthcare and identification of key attributes affecting healthcare. The choice of the design for a DCE is critical because it determines which attributes' effects and their interactions are identifiable. We apply blocked fractional factorial designs to construct DCEs and address some identification issues by utilizing the known structure of blocked fractional factorial designs. Our design techniques can be applied to several situations including DCEs where attributes have different number of levels. We demonstrate our design methodology using two healthcare studies to evaluate (i) asthma patients' preferences for symptom-based outcome measures and (ii) patient preference for breast screening services. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Mathematics, California State University, Fullerton, 92831, CA, U.S.A.
authors.forename: Jessica
authors.initials: J
authors.lastname: Jaynes
authors.affiliation: Department of Biostatistics, University of California, Los Angeles, 90095, CA, U.S.A.
authors.forename: Weng-Kee
authors.initials: WK
authors.lastname: Wong
authors.affiliation: Department of Statistics, University of California, Los Angeles, 90095, CA, U.S.A.
authors.forename: Hongquan
authors.initials: H
authors.lastname: Xu
mesh_terms: D002755:Choice Behavior; D003657:Decision Making; D003695:Delivery of Health Care; D006801:Humans; D057240:Patient Preference; D012107:Research Design
keywords: aliasing; blocked fractional factorial designs; interaction effects; locally optimal designs; multinomial logit model
references.citation: BMC Public Health. 2013 Nov 29;13:1099
references.pmid: 24289831
references.citation: Epilepsy Behav. 2014 Feb;31:102-9
references.pmid: 24389020
references.citation: Health Policy. 2001 Sep;57(3):193-204
references.pmid: 11459626
references.citation: Health Econ. 2012 Feb;21(2):145-72
references.pmid: 22223558
references.citation: Pharmacoeconomics. 2008;26(8):661-77
references.pmid: 18620460
references.citation: Am J Public Health. 2009 Sep;99(9):1666-72
references.pmid: 19608959
references.citation: Health Expect. 2015 Dec;18(6):2223-35
references.pmid: 24720861
references.citation: Value Health. 2011 Jun;14(4):403-13
references.pmid: 21669364
references.citation: Health Econ. 2011 Sep;20 Suppl 1:35-52
references.pmid: 21809412
references.citation: Arthritis Res Ther. 2014 Jan 31;16(1):R36
references.pmid: 24479410
references.citation: BMJ Open. 2014 Apr 03;4(4):e004327
references.pmid: 24699460
references.citation: BMC Health Serv Res. 2012 Jul 23;12:212
references.pmid: 22824497
references.citation: BMC Health Serv Res. 2012 Jun 14;12:159
references.pmid: 22697498
references.citation: BMC Health Serv Res. 2014 Sep 01;14:367
references.pmid: 25179422
references.citation: Value Health. 2013 Jan-Feb;16(1):3-13
references.pmid: 23337210
references.citation: Am J Public Health. 2008 Aug;98(8):1354-9
references.pmid: 18556602
references.citation: J Clin Med. 2014 Feb 14;3(1):176-90
references.pmid: 26237256
references.citation: Health Econ Policy Law. 2009 Oct;4(Pt 4):527-46
references.pmid: 19715635
references.citation: Int Stat Rev. 2015 Aug 1;83(2):239-262
references.pmid: 26512159
references.citation: BMJ. 2004 Feb 14;328(7436):360-1
references.pmid: 14962852
references.citation: Expert Rev Pharmacoecon Outcomes Res. 2002 Aug;2(4):319-26
references.pmid: 19807438
references.citation: Pharmacoeconomics. 2014 Sep;32(9):883-902
references.pmid: 25005924
references.citation: BMJ. 2000 Jun 3;320(7248):1530-3
references.pmid: 10834905
references.citation: Health Econ. 2014 Jun;23(6):729-42
references.pmid: 23765787