Author, Subjects, Keywords

Cited Author

 
 
   » By Author or Editor
 » Browse Author by Alphabet
 » By Journal
 » By Subjects
 » By Affiliations
 » By Type
 » By Year
 » By Latest Additions
 
 
   » By Author
 » Top 20 Authors
 » Top 20 Article
 » Top 20 Journal Cited
 » Top 20 Cited
 » Top 20 Author Cited
 » Usage Since Sept 2007


 
 
 

Login | Create Account

Conjoint Analysis: An Application in Eliciting Patients' Preferences

Yen, S.H., (2006) Conjoint Analysis: An Application in Eliciting Patients' Preferences. Bulletin of the Malaysian Mathematical Sciences Society, 29 (2). pp. 187-201. ISSN 0126-6705

Full text not available from this repository.

Official URL: http://math.usm.my/bulletin/pdf/v29n2/v29n2p10.pdf

Affiliations

Universiti Sains Malaysia, School of Distance Education, Economics Section

Abstract

Conjoint analysis is a technique for establishing the relative importance of different attributes in the provision of a good or a service. In this study conjoint analysis was applied to characterize diabetic patients' preferences for information during doctor-patient interactions. Patients' utility function was further developed based on the random utility model that would account for inconsistencies that arises in patients' choice behaviors. The unobserved portions of the utility function were specified as a combination of an IID (Independently & Identically Distributed) distribution and another general distribution allows the model to be specified as mixed logit. The mixed logit approach provides an efficient estimate of correlation of the unobserved portions of patients' utility function due to repeated choices made by the same respondent. Results from the analysis can be interpreted in terms of marginal rate of substitution (MRS) between attributes. Socio-economic characteristics of the patient were introduced into the model in the form of interaction terms explained how preferences varied across patients.

Item Type:Journal
Keywords:conjoint analysis, preferences, choice behavior, random utility theory, mixed logit.
Subjects:Q Science, Computer Science
ID Code:1447

[1] W. Adamowicz, J. Louviere and M. Williams, Combining revealed preference and stated preference methods for valuing environmental amenities, J. Environ. Econ. Manage. 6 (1994), 271-292.

[2] S. Algers, P. Bergstrom , M. Dahlberg & J.L. Dillen, Mixed logit estimation of the value of travel time, Working Paper Series No:1998:15, Department of Economics, Uppsala University, 1998.

[3] M. Ben-Akiva and M. Bierlaire, Discrete choice methods and their applications to short-term travel decisions, In Handbook of Transportation Science (R. Hall., ed), International Series in Operations Research and Management Science, Vol. 23. Kluwer, 1999.

[4] A. J. Carr and J. L. Donovan, Why doctors and patients disagree, British J. Rheumatology 37(1) (1998), 1-4.

[5] C. Charles, A. Gafni and T. Whelan, Decision-making in the physician-patient encounter: revisiting the shared treatment decision-making model, Soc. Sci. Medicine 49 (1999), 651-661.

[6] M.A. Graf , D.S. Tanner and W.R. Swinyard, Optimizing the delivery of patient and physician satisfaction: a conjoint analysis approach, Health Care Manage. Rev. 18(4) (1993), 34-43.

[7] P.E. Green and Y. Wind, New way to measure consumer’s judgements, Harvard Bus. Rev. 53 (1975), 107-117.

[8] P.E. Green and V. Srinivasan, Conjoint analysis in marketing: new developments with implications for research and practice, J. Marketing 54(4) (1990), 3—19.

[9] J.F. Hair, R.E. Anderson, R.L. Tatnam and W.C. Black, Multivariate Data Analysis, 5th ed. New Jersey: Prentice Hall, Inc., 1998.

[10] J.A. Hall and M.C. Dornan, What patients like about their medical care and how often they are asked: a meta-analysis of the satisfaction literature, Soc. Sci. Medicine 27(9) (1988), 935-939.

[11] J.A. Hall, D.L. Roter, and N.R. Katz, Meta-analysis of correlates of provider behavior in medical encounters. Medical Care 26(7) (1988), 657-675.

[12] D. Hedekar, MIXNO: A Computer Program for Mixed-Effects Nominal Logistic Regression, School of Public Health, University of Illinois at Chicago, 1999.

[13] J.D. Hey, Uncertainty in Microeconomics, Oxford: Martin Robertson. (1979).

[14] S. Jan, G. Mooney, M. Ryan, K. Bruggemann, and K. Alexander, The use of conjoint analysis to elicit community preferences in public health research: a case study of hospital services in South Australia, Austral. New Zealand J. Public Health 24(1) (2000), 64-70.

[15] L.H. Labson, Using word to improve your patient’s health, Patient Care 34(5) (2000), 133-140.

[16] K.J. Lancaster, Consumer Demand: A New Approach. New York: Colombia University Press, 1971.

[17] J.J. Louviere, Analyzing Decision Making: Metric Conjoint Analysis, Series on Quantitative Application in Soc. Science, Vol. 67. California: SAGE publications, 1988.

[18] R.D. Luce and J.W. Tukey, Simultaneous conjoint measurement: a new type of fundamental measurement. J. Math. Psycholoqy 1 (1964), 1—27.

[19] D. McFadden, The measnrement of urban travel demand, J. Public Econ. 3 (197.1), 303-328.

[20] D. McFadden and K. Train, Mixed MNL model for discrete response, J. Appl. Econometrics 15(15),(2000) 417-470.

[21] A. McGuire, J. Henderson and C. Mooney, The Economics of Health Care: An Introductory Text, London: Routledge, 1992.

[22] F.S. Miguel and M. Ryan, Applying conjoint analysis in economic evaluation: an application to menorrhagia, Applied Econ. 32(7) (2000), 823 834.

[23] MIXNO The program for analyzing mixed-effects nominal logistic regression. URL: http://www.uic.edu/hedeker/mix/html

[24] J. Opaluch, S. Swallow, T. Weaver, C. Wessells and D. Wichelns, Evaluating impacts from noxious facilities: including public preferences in current sitting mechanism, J. Environ. Econ. Manag. 24 (1993). 41-59.

[25] D. Revelt and K. Train, Mixed logit with repeated choices: households’ choices of appliance efficiency level, Rev. Econ. Statist. 80(4) (1998), 647-664.

[26] B. Roe and K.J. Boyle, Using conjoint analysis to derive estimates of compensating variation, J. Environ. Econ. Manage. 31(2) (1996), 145-160.

[27] M. Ryan, J. Ratcliffe and J. Tucker, Using willingness to pay to value alternative models of antenatal care, Soc. Sci. Medicine 44(3) (1997), 371-380.

[28] M. Ryan and J. Hughes. Using conjoint analysis to assess women’s preferences for miscarriage management, Health Econ. 6(3) (1997), 262-273.

[29] M. Ryan, Using conjoint analysis to take account of patient preferences and go beyond health outcomes: an application to in-vitro fertilization, Soc. Sci. Medicine 48(4) (1999), 535-546.

[30] M. Ryan, Conjoint Analysis: A New Tool for Eliciting Patients’ Preferences Briefing papers for the NHS in Scotland. No: 6. Health Economics Research Unit, University of Aberdeen. 1999.

[31] M. Ryan and S. Farrar, Using conjoint analysis to elicit preferences for health care, British Medical J. 320(7248) (2000), 1530-1533.

[32] M. Ryan, A. Bate, C.J. Eastmond and A. Ludbrook, Use of discrete choice experiments to elicit preferences, Quality in Health Care 10 (2001), 155-163.

[33] G. Salkeld, M. Ryan and L. Short, The veil of experience. Do consumers prefer what they know best? Health Econ. 4(1) (2000), 4-9.

[34] A. Scott and S. Vick, Patients, doctors and contracts: an application of principal-agent theory to the doctor-patient relationship, Scottish J. Political Economy 46(2) (1999), 111-134.

[35] A. Scott, M.S. Watson and S. Ross, Eliciting preferences of the community for out of hours care provided by general practitioners: a stated preference discrete choice experiment. Soc. Sci. Medicine (2002) Available online 19 March 2002.

[36] F.A. Stevenson, C.A. Barry, N. Britten, N. Barber and C. P. Bradley, Doctor-patient communication about drugs: the evidence for shared decision making, Soc. Sci. Medicine 50(6) (20110), 829-840.

[37] J.R. Street, Information—giving in medical consultations: the influence of patients’ communicative styles and personal characteristics, Soc. Sci. Medicine 32(5) (1991), 541-548.

[38] S. Vick and A. Scott, Agency in health care. Examining patient’s preferences for attributes of doctor-patient relationship. J. Health Econ. 17(5) (1998), 586-605.

[39] M. Wardman, A comparison of revealed preference and stated preference models, J. Transport Econ. Policy 22 (1988), 71—91.

[40] D.R. Wittink and P. Cattin, Commercial use of conjoint analysis: an update, J. Marketing 53 (1989), 91—96.

Repository Staff Only: item control page
 
 
 
 
 
   
© Copyright 2007 MyAIS Faculty of Computer Science and Information Technology, University of Malaya – All Rights Reserved. Malaysian Abstracting and Indexing System is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.