Discrete choice experiments of pharmacy services: a systematic review
Background Two previous systematic reviews have summarised the application of discrete choice experiments to value preferences for pharmacy services. These reviews identified a total of twelve studies and described how discrete choice experiments have been used to value pharmacy services but did not describe or discuss the application of methods used in the design or analysis. Aims (1) To update the most recent systematic review and critically appraise current discrete choice experiments of pharmacy services in line with published reporting criteria and; (2) To provide an overview of key methodological developments in the design and analysis of discrete choice experiments. Methods The review used a comprehensive strategy to identify eligible studies (published between 1990 and 2015) by searching electronic databases for key terms related to discrete choice and best–worst scaling (BWS) experiments. All healthcare choice experiments were then hand-searched for key terms relating to pharmacy. Data were extracted using a published checklist. Results A total of 17 discrete choice experiments eliciting preferences for pharmacy services were identified for inclusion in the review. No BWS studies were identified. The studies elicited preferences from a variety of populations (pharmacists, patients, students) for a range of pharmacy services. Most studies were from a United Kingdom setting, although examples from Europe, Australia and North America were also identified. Discrete choice experiments for pharmacy services tended to include more attributes than non-pharmacy choice experiments. Few studies reported the use of qualitative research methods in the design and interpretation of the experiments (n = 9) or use of new methods of analysis to identify and quantify preference and scale heterogeneity (n = 4). No studies reported the use of Bayesian methods in their experimental design. Conclusion Incorporating more sophisticated methods in the design of pharmacy-related discrete choice experiments could help researchers produce more efficient experiments which are better suited to valuing complex pharmacy services. Pharmacy-related discrete choice experiments could also benefit from more sophisticated analytical techniques such as investigations into scale and preference heterogeneity. Employing these sophisticated methods for both design and analysis could extend the usefulness of discrete choice experiments to inform health and pharmacy policy.
KeywordsBest–worst scaling Discrete choice experiment Preferences Review Values
The research for this paper was made possible by a Grant to the project Mind the Risk from The Swedish Foundation for Humanities and Social Sciences. The authors would like to thank Mirella Longo for contributing feedback on the review protocol.
Conflicts of interest
The Authors have no conflicts of interest to disclose. The authors are responsible for the content and writing of the paper.
- 4.Cutler S, Fattah L, Shaw M, Cutts C. What does medicines optimisation mean for pharmacy professionals? Pharm J. 2011;287(7680):606.Google Scholar
- 8.Perepelkin J. Public opinion of pharmacists and pharmacist prescribing. Can J Surg. 2011;144(2):86–93.Google Scholar
- 12.Ryan M, Gerard K. Using discrete choice experiments in health economics: moving forward. In: Scott A, Maynard A, Elliott R, editors. Advances in health economics. Chichester: Wiley; 2003. p. 25–40.Google Scholar
- 17.McIntosh E, Louviere J. Separating weight and scale value: an exploration of best-attribute scaling in health economics. In: Health Economists’ Study Group Meeting. Brunel University; 2002.Google Scholar
- 23.Centre for Reviews and Dissemination (CRD). Systematic Reviews: CRD's guidance for undertaking reviews in health care. York: The University of York; 2008.Google Scholar
- 29.Laba T, Brien J. Patient preferences for adherence to treatment for osteoarthritis: the MEdication Decisions in Osteoarthritis Study (MEDOS). BMC Musculoskelet Disord. 2013;14(160):1–9.Google Scholar
- 39.Yi D, Ryan M, Campbell S, Elliott A, Torrance N, Chambers A, et al. Using discrete choice experiments to inform randomised controlled trials: an application to chronic low back pain management in primary care. Eur J Pain. 2011;15(5):510–31.Google Scholar
- 52.Scalone L, Mantovani LG, Borghetti F, von Mackensen S, Gringeri A, Barillari G, et al. Patients’, physicians’, and pharmacists’ preferences towards coagulation factor concentrates to treat haemophilia with inhibitors: results from the COHIBA Study. Haemophilia. 2009;15(2):473–86.CrossRefPubMedGoogle Scholar
- 67.Zwerina K, Huber J, Kuhfeld W. A general method for constructing efficient choice designs. SAS Technical Document TS-722E. 1996. p. 265–83.Google Scholar
- 75.De Bekker-Grob EW, Rose JM, Bliemer MCJ. A closer look at decision and analyst error by including nonlinearities in discrete choice models: implications on willingness-to-pay estimates derived from discrete choice data in healthcare. Pharmacoeconomics. 2013;31(12):1169–83.CrossRefPubMedGoogle Scholar
- 77.Vass C, Rigby D, Campbell S, Tate K, Stewart A, Payne K. Investigating the framing of risk attributes in a discrete choice experiment: an application of eye-tracking and think aloud. Med Decis Mak. 2014;35(1):E99.Google Scholar