Probabilistic choice models in health-state valuation research: Background, theories, assumptions and applications

Expert Review of Pharmacoeconomics and Outcomes Research

Volumn 13, Issue 1, 2013, Pages 93-108

  Arons, Alexander M M ^a   Krabbe, Paul F M ^b  

^a RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^b UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

Author keywords

choice models; health state; quantification; subjective measurement; utilities; values

Indexed keywords

BRADLY TERRY LUCE MODEL; CONCEPTUAL FRAMEWORK; CONJOINT ANALYSIS; DISCRETE CHOICE MODEL; HEALTH ASSESSMENT QUESTIONNAIRE; HEALTH ECONOMICS; HEALTH SERVICE; HUMAN; INTERMETHOD COMPARISON; MATHEMATICAL ANALYSIS; MEDICAL RESEARCH; NEEDS ASSESSMENT; PROBABILISTIC CHOICE MODEL; QUALITY OF LIFE; QUANTITATIVE ANALYSIS; RASCH ANALYSIS; REVIEW; STATISTICAL ANALYSIS; STATISTICAL MODEL; TREATMENT OUTCOME;

CHOICE BEHAVIOR; COSTS AND COST ANALYSIS; DATA INTERPRETATION, STATISTICAL; DISABILITY EVALUATION; HEALTH CARE COSTS; HEALTH SERVICES RESEARCH; HEALTH STATUS; HEALTH STATUS INDICATORS; HUMANS; MODELS, STATISTICAL; OUTCOME AND PROCESS ASSESSMENT (HEALTH CARE); PATIENT PREFERENCE; PROBABILITY; QUALITY OF LIFE; RECOVERY OF FUNCTION; TREATMENT OUTCOME;

EID: 84873891691     PISSN: 14737167     EISSN: 17448379     Source Type: Journal    
DOI: 10.1586/erp.12.85     Document Type: Review

References (91)

1. Ryan M, Bate A, Eastmond CJ, Ludbrook A. Use of discrete choice experiments to elicit preferences. Qual. Health Care 10(Suppl. 1), i55-i60 (2001).
2. Hakim Z, Pathak DS. Modelling the EuroQol data: a comparison of discrete choice conjoint and conditional preference modelling. Health Econ. 8(2), 103-116 (1999).
3. McCabe C, Brazier J, Gilks P et al. Using rank data to estimate health state utility models. J. Health Econ. 25(3), 418-431 (2006).
4. McKenzie L, Cairns J, Osman L. Symptom-based outcome measures for asthma: the use of discrete choice methods to assess patient preferences. Health Policy 57(3), 193-204 (2001).
5. Ratcliffe J, Brazier J, Tsuchiya A, Symonds T, Brown M. Using DCE and ranking data to estimate cardinal values for health states for deriving a preference-based single index from the sexual quality of life questionnaire. Health Econ. 18(11), 1261-1276 (2009).
6. Ryan M, Netten A, Skåtun D, Smith P. Using discrete choice experiments to estimate a preference-based measure of outcome-an application to social care for older people. J. Health Econ. 25(5), 927-944 (2006).
7. Szeinbach SL, Barnes JH, McGhan WF, Murawski MM, Corey R. Using conjoint analysis to evaluate health state preferences. Drug Inf. J. 33(3), 849-858 (1999).
8. Louviere JJ, Hensher DA, Swait JD. Stated Choice Methods. Cambridge University press, Cambridge, UK (2000).
9. Ryan M, Gerard K, Amaya-Amaya M. Using Discrete Choice Experiments to Value Health and Health Care. Springer Academic Publishers, Berlin, Germany (2008).
10. McFadden D. Conditional logit analysis of qualitative choice behavior. In: Frontiers in Econometrics. Zarembka P. Academic Press, San Diego, CA, USA, 105-142 (1974).
11. Ben-Akiva ME, Lerman SR. Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press, Cambridge, U K (1985).
12. Bridges JF, Hauber AB, Marshall D et al. Conjoint analysis applications in health-A checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health 14(4), 403-413 (2011).
13. Cunningham CE, Deal K, Rimas H, Chen Y, Buchanan DH, Sdao-Jarvie K. Providing information to parents of children with mental health problems: a discrete choice conjoint analysis of professional preferences. J. Abnorm. Child Psychol. 37(8), 1089-1102 (2009).
14. Hall J, Viney R, Haas M, Louviere J. Using stated preference discrete choice modeling to evaluate health care programs. J. Bus. Res. 57(9), 1026-1032 (2004).
15. Swait JD. Choice models based on mixed discrete/continuous PDFs. Transport. Res. Part B Meth. 43(7), 766-783 (2009).
16. Thurstone LL. A law of comparative judgment (Reprinted from Psychological Review, Vol 34, Pg 273, 1927). Psychol. Rev. 101(2), 266-270 (1994).
17. Stevens SS. On the psychophysical law. Psychol. Rev. 64(3), 153-181 (1957).
18. Bradley RA, Terry ME. Rank analysis of incomplete block designs. 1. The method of paired comparisons. Biometrika 39(3-4), 324-345 (1952).
19. Luce RD. Individual Choice Behavior: a Theoretical Analysis. Wiley, New York, NY, USA (1959).
20. Kind P. A comparison of two models for scaling health indicators. Int. J. Epidemiol. 11(3), 271-275 (1982).
21. Luce RD, Suppes P. Preference, utility, and subjective probability. In: Handbook of mathematical psychology. Luce RD, Bush RR, Galanter E (Eds). John Wiley & Sons, New York, NY, USA, 235-406 (1965).
22. Luce RD, Tukey JW. Simultaneous conjoint-measurement-A new type of fundamental measurement. J. Math. Psychol. 1(1), 1-27 (1964).
23. Perline R, Wright BD, Wainer H. The Rasch model as additive conjoint measurement. Appl. Psychol. Meas. 3(2), 237-255 (1979).
24. Maas A, Stalpers L. Assessing utilities by means of conjoint measurement: an application in medical decision analysis. Med. Decis. Making 12(4), 288-297 (1992).
25. Stalmeier PF, Bezembinder TG, Unic IJ. Proportional heuristics in time tradeoff and conjoint measurement. Med. Decis. Making 16(1), 36-44 (1996).
26. Rasch G. An item analysis which takes individual differences into account. Br. J. Math. Stat. Psychol. 19(1), 49-57 (1966).
27. Holman R, Weisscher N, Glas CA et al The Academic Medical Center Linear Disability Score (ALDS) item bank: item response theory analysis in a mixed patient population. Health Qual. Life Outcomes 3, 83 (2005).
28. Schultz-Larsen K, Kreiner S, Lomholt RK. Mini-Mental Status Examination: mixed Rasch model item analysis derived two different cognitive dimensions of the MMSE. J. Clin. Epidemiol. 60(3), 268-279 (2007).
29. Brazier J, Czoski-Murray C, Roberts J, Brown M, Symonds T, Kelleher C. Estimation of a preference-based index from a condition-specifc measure: the King's Health Questionnaire. Med. Decis. Making 28(1), 113-126 (2008).
30. Young T, Yang Y, Brazier JE, Tsuchiya A, Coyne K. The frst stage of developing preference-based measures: constructing a health-state classifcation using Rasch analysis. Qual. Life Res. 18(2), 253-265 (2009).
31. Young TA, Yang Y, Brazier JE, Tsuchiya A. The use of rasch analysis in reducing a large condition-specifc instrument for preference valuation: the case of moving from AQLQ to AQL-5D. Med. Decis. Making 31(1), 195-210 (2011).
32. Krabbe PFM. A generalized measurement model to quantify health: the multi-attrib ute preference response model. Proceedings of the 29th Plenary Meeting of the Euro Qol Group, Conference Paper. Rotterdam, The Netherlands, 13-14 September 2012.
33. Brogden HE. Rasch model, law of comparative judgment and additive conjoint measurement. Psychometrika 42(4), 631-634 (1977).
34. Andrich D. Relationships between the Thurstone and Rasch approaches to item scaling. Appl. Psychol. Meas. 2(3), 451-462 (1978).
35. Guttman L. The basis for Scalogram analysis. In: Measurement & Prediction, The American Soldier. Stouffer SA, Suchman A, Leland C et al. (Eds). Wiley, NY, USA (1950).
36. Coombs CH. A Theory of Data. John Wiley & Sons, NY, USA (1964).
37. Green PE, Rao VR. Conjoint measurement for quantifying judgmental data. J. Market. Res. 8(3), 355-363 (1971).
38. Louviere JJ, Flynn TN, Carson RT Discrete choice experiments are not conjoint analysis. J. Choice Model. 3(3), 57-72 (2010).
39. Marschak J. Binary-choice constraints and random utility indicators. In: Mathematical Methods in the Social Sciences. Arrow K, Karlin S, Suppes P (Eds). Stanford University Press, Stanford, Germany, 312-329 (1960).
40. McFadden D. Econometric models for probabilistic choice among products. J. Bus. 53(3), S13-S29 (1980).
41. McFadden D. Economic choices. Am.Econ. Rev. 91(3), 351-378 (2001).
42. Salomon JA. Reconsidering the use of rankings in the valuation of health states: a model for estimating cardinal values from ordinal data. Popul. Health Metr. 1(1), 12 (2003).
43. Lancaster KJ. A new approach to consumer theory. J. Politic. Econ. 74, 132-157 (1966).
44. Louviere JJ, Woodworth G. Design and analysis of simulated consumer choice or allocation experiments-an approach based on aggregate data. J. Market. Res. 20(4), 350-367 (1983).
45. Fanshel S, Bush JW. A health-status index and its application to health-services outcomes. Oper. Res. 18(6), 1021-1066 (1970).
46. Hadorn DC, Hays RD, Uebersax J, Hauber T Improving task comprehension in the measurement of health state preferences. A trial of informational cartoon fgures and a paired-comparison task. J. Clin. Epidemiol. 45(3), 233-243 (1992).
47. Krabbe PF. Thurstone scaling as a measurement method to quantify subjective health outcomes. Med. Care 46(4), 357-365 (2008).
48. Stolk EA, Oppe M, Scalone L, Krabbe PF. Discrete choice modeling for the quantif-cation of health states: the case of the EQ-5D. Value Health 13(8), 1005-1013 (2010).
49. Rabin R, de Charro F. EQ-5D: a measure of health status from the EuroQol Group. Ann. Med. 33(5), 337-343 (2001).
50. Coast J, Flynn TN, Natarajan L et al. Valuing the ICECAP capability index for older people. Soc. Sci. Med. 67(5), 874-882 (2008).
51. Brazier J, Rowen D, Yang Y, Tsuchiya A. Comparison of health state utility values derived using time trade-off, rank and discrete choice data anchored on the full health-dead scale. Eur. J. Health Econ. 13(5), 575-587 (2012).
52. Craig BM, Busschbach JJ. The episodic random utility model unifes time trade-off and discrete choice approaches in health state valuation. Popul. Health Metr. 7, 3 (2009).
53. Craig BM, Busschbach J, Salomon JA. Ordinal valuation of health states: a seven country comparison. Proceedings of the EuroQol plenary meeting. Barcelona, Spain September 14-162006
54. Maydeu-Olivares A, Böckenholt U. Structural equation modeling of paired-comparison and ranking data. Psychol. Methods 10(3), 285-304 (2005).
55. Maydeu-Olivares A, Böckenholt U. Modeling subjective health outcomes: top 10 reasons to use Thurstone's method. Med. Care 46(4), 346-348 (2008).
56. Xie F, Pullenayegum E, Gaebel K, Oppe M, Krabbe PFM. Eliciting preferences to the EQ-5D-5L health states: discrete choice experiment or multiprofle case of best-worst scaling. Value Health 15(4), A198-A199 (2012).
57. Andrich D. Controversy and the Rasch model: a characteristic of incompatible paradigms? Med. Care 42(1 Suppl.), I7-16 (2004).
58. Flynn TN, Louviere JJ, Peters TJ, Coast J. Using discrete choice experiments to understand preferences for quality of life. Variance-scale heterogeneity matters. Soc. Sci. Med. 70(12), 1957-1965 (2010).
59. Flynn TN, Louviere JJ, Peters TJ, Coast J. Best-worst scaling: what it can do for health care research and how to do it. J. Health Econ. 26(1), 171-189 (2007).
60. Flynn TN, Louviere JJ, Marley AA, Coast J, Peters TJ. Rescaling quality of life values from discrete choice experiments for use as QALYs: a cautionary tale. Popul. Health Metr. 6, 6 (2008).
61. Marley AAJ, Louviere JJ. Some probabilistic models of best, worst, and best-worst choices. J. Math. Psychol. 49(6), 464-480 (2005).
62. Finn A, Louviere JJ. Determining the appropriate response to evidence of public concern: the case of food safety. J. Public Policy Market 11, 12-25 (1992).
63. Ratcliffe J, Couzner L, Flynn T et al Valuing Child Health Utility 9D health states with a young adolescent sample: a feasibility study to compare best-worst scaling discrete-choice experiment, standard gamble and time trade-off methods. Appl. Health Econ. Health Policy 9(1), 15-27 (2011).
64. Flynn TN. Valuing citizen and patient preferences in health: recent developments in three types of best-worst scaling. Expert Rev. Pharmacoecon. Outcomes Res. 10(3), 259-267 (2010).
65. Bleichrodt H, Johannesson M. The validity of QALYs: an experimental test of constant proportional tradeoff and utility independence. Med. Decis. Making 17(1), 21-32 (1997).
66. Nord E. Methods for quality adjustment of life years. Soc. Sci. Med. 34(5), 559-569 (1992).
67. Froberg DG, Kane RL. Methodology for measuring health-state preferences-II: Scaling methods. J. Clin. Epidemiol. 42(5), 459-471 (1989).
68. Bleichrodt H. A new explanation for the difference between time trade-off utilities and standard gamble utilities. Health Econ. 11(5), 447-456 (2002).
69. Stalmeier PF, Lamers LM, Busschbach JJ, Krabbe PF. On the assessment of preferences for health and duration: maximal endurable time and better than dead preferences. Med. Care 45(9), 835-841 (2007).
70. Boyle KJ, Bishop RC, Welsh MP. Starting point bias in contingent valuation bidding games. Land Econ. 61(2), 188-194 (1985).
71. Bansback N, Brazier J, Tsuchiya A, Anis A. Using a discrete choice experiment to estimate health state utility values. J. Health Econ. 31(1), 306-318 (2012).
72. Oppe M, Devlin N, Van Hout B, Krabbe PFM, De Charro FTH. EuroQol Group's international protocol for the valuation of the EQ-5D-5L. Presented at: 29th Plenary Meeting of the EuroQol Group. Rotterdam, The Netherlands, September 13-142012.
73. Krabbe PF, Stalmeier PF, Lamers LM, Busschbach JJ. Testing the interval-level measurement property of multi-item visual analogue scales. Qual. Life Res. 15(10), 1651-1661 (2006).
74. Parkin D, Devlin N. Is there a case for using visual analogue scale valuations in cost-utility analysis? Health Econ. 15 (7), 653-664 (2006).
75. Robinson A, Loomes G, Jones-Lee M. Visual analog scales, standard gambles, and relative risk aversion. Med. Decis. Making 21(1), 17-27 (2001).
76. Schwartz A. Rating scales in context. Med. Decis. Making 18(2), 236 (1998).
77. Torrance GW, Feeny D, Furlong W. Visual analog scales: do they have a role in the measurement of preferences for health states? Med. Decis. Making 21(4), 329-334 (2001).
78. Bleichrodt H, Johannesson M. An experimental test of a theoretical foundation for rating-scale valuations. Med. Decis. Making 17(2), 208-216 (1997).
79. McPhail S, Beller E, Haines T Reference bias: presentation of extreme health states prior to EQ-VAS improves health-related quality of life scores. A randomised cross-over trial. Health Qual. Life Outcomes 8, 146 (2010).
80. Ubel PA, Jankovic A, Smith D, Langa KM, Fagerlin A. What is perfect health to an 85-year-old? Evidence for scale recalibra-tion in subjective health ratings. Med. Care 43(10), 1054-1057 (2005).
81. Bryan S, Dolan P. Discrete choice experiments in health economics. For better or for worse? Eur. J. Health Econ. 5(3), 199-202 (2004).
82. Nunnally JC, Bernstein IH. Psychometric Theory. McGraw-Hill, New York, NY, USA (1994).
83. Krabbe PF, Salomon JA, Murray CJ. Quantifcation of health states with rank-based nonmetric multidimensional scaling. Med. Decis. Making 27(4), 395-405 (2007).
84. Torgerson WS. Theory and Methods of Scaling. Oxford, England (1958).
85. De Ayala RJ. The Theory and Practice of Item Response Theory. Guilford Publications, New York, NY, USA (2008).
86. Lord FM. Applications of Item Response Theory to Practical Testing Problems. Erlbaum Associates, Abingdon, UK (1980).
87. Maydeu-Olivares A. Thurstonian modeling of ranking data via mean and covariance structure analysis. Psychometrika 64(3), 325-340 (1999).
88. Engelhard G. Historical views of invari-ance-evidence from the measurement theories of Thorndike, Thurstone, and Rasch. Educ. Psychol. Meas. 52(2), 275-291 (1992).
89. Debreu G. Review of R.D. Luce's individual choice behavior: a theoretical analysis. Am. Econ. Rev. 50, 186-188 (1960).
90. Arrow K. Social Choice and Individual Values, Second edition (Cowles Foundation Monographs Series). Yale University Press, New Haven, CT, USA (1970).
91. McFaden D, Train K. Mixed MNL models for discrete response. J. Appl. Econom. 15(5), 447-470 (2000).