Health Status Utility Assessment by Standard Gamble: A Comparison of the Probability Equivalence and the Lottery Equivalence Approaches
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Purpose. Utility values obtained with the standard gamble (SG) method using the probability equivalence approach (PE) have a reported bias due to the "certainty effect." This effect causes individuals to overvalue a positive outcome when it occurs under certainty. Researchers in the decision sciences have proposed an alternative, "lottery equivalence” (LE) approach, using paired gambles, to eliminate this bias. The major objective of the current study was to investigate the certainty effect in health status utility measures and to test our hypothesis that the certainty effect would act in a reverse direction for negatively valued outcomes.
Methods. Fifty-four subjects completed the study by assessing preferences for three health states by rating scale and then by SG using PE as well as LE approaches with assessment lotteries of 0.5 and 0.75.
Results. The results from 41 useable responses point towards possible existence of the certainty effect in health in the hypothesized direction: utility values obtained with the PE were significantly lower than with the LEs. There was no significant difference between the LE values indicating elimination of the bias.
Conclusions. The results have important implications since the SG using PE is thought be the "gold standard” in health status utility measurements.
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- 1.G. W. Torrance. J. Chron. Dis. 40(6):593–600 (1987).Google Scholar
- 2.G. W. Torrance. J. Health Econ. 5:1–30 (1986).Google Scholar
- 3.J. von Neumann and O. Morgernstern. Theory of games and economic behavior, 2nd edition, Princeton University Press, New York. 1947.Google Scholar
- 4.P. H. Farquhar. Mgmt. Sci. 30(11):1283–1300 (Nov. 1984).Google Scholar
- 5.M. R. McCord and R. deNeufville. Mgmt. Sci. 32(1):56–60 (Jan. 1986).Google Scholar
- 6.M. Allais. In M. Allais, and O. Hagen, (eds.). Expected utility hypotheses and the Allais paradox, D. Reidel Publishing Company, Holland, 1979, pp. 437–482.Google Scholar
- 7.J. C. Hershey, H. C. Kunreuther, and P. J. H. Schoemaker. Mgmt. Sci. 28(8):936–954 (Aug. 1982).Google Scholar
- 8.R. T. Clemen. In Making Hard Decisions: An Introduction to Decision Analysis. PWS-Kent, Boston, 1991.Google Scholar
- 9.M. R. McCord and R. deNeufville. J. Large Scale Sys. 6:91–103 (1984).Google Scholar
- 10.G. W. Torrance. Socioeconomic Planning Science. 10:129–136 (1976).Google Scholar
- 11.M. F. Drummond, G. L. Stoddart, and G. W. Torrance. In Methods for the economic evaluation of health care programmes. Oxford, 1992, pp. 112–148.Google Scholar
- 12.D. L. Sackett and G. W. Torrance. J. Chron. Dis. 31:697–704 (1978).Google Scholar
- 13.T. R. Bowe. Medical Decision Making. 15(3):283–285 (Jul–Sep 1995).Google Scholar
- 14.W. Furlong, D. Feeny, G. W. Torrance, R. Barr, and J. Horsman. Guide to design and development of health-state utility instrumentation. working Paper Series. Ontario, Canada: Center for Health Economics and Policy Analysis, McMaster University, 1990, Paper#90-9.Google Scholar
- 15.L. C. G. Verhoef, A. F. J. DeHaan, and W. A. J. Van Daal. Medical Decision Making. 14:194–200, 1994.Google Scholar
- 16.Z. A. Hakim. Unpublished Ph.D. Dissertation, The Ohio State University, 1995.Google Scholar
- 17.R. E. Kirk. Experimental Design: Procedures for the behavioral sciences. Second edition. Brooks/Cole, California, 1982.Google Scholar
- 18.D. Kahneman and A. Tversky. Econometrica. 47:263–291 (1979).Google Scholar
- 19.G. Loomes and R. Sugden. Economics Journal. 92:805 (1982).Google Scholar
- 20.G. Loomes and R. Sugden. J. Econ. Theory. 41:270 (1987).Google Scholar