Valuing SF-6D Health States Using a Discrete Choice Experiment

Med Decis Making. 2014 Aug;34(6):773-86. doi: 10.1177/0272989X13503499. Epub 2013 Sep 11.


Background: SF-6D utility weights are conventionally produced using a standard gamble (SG). SG-derived weights consistently demonstrate a floor effect not observed with other elicitation techniques. Recent advances in discrete choice methods have allowed estimation of utility weights. The objective was to produce Australian utility weights for the SF-6D and to explore the application of discrete choice experiment (DCE) methods in this context. We hypothesized that weights derived using this method would reflect the largely monotonic construction of the SF-6D.

Methods: We designed an online DCE and administered it to an Australia-representative online panel (n = 1017). A range of specifications investigating nonlinear preferences with respect to additional life expectancy were estimated using a random-effects probit model. The preferred model was then used to estimate a preference index such that full health and death were valued at 1 and 0, respectively, to provide an algorithm for Australian cost-utility analyses.

Results: Physical functioning, pain, mental health, and vitality were the largest drivers of utility weights. Combining levels to remove illogical orderings did not lead to a poorer model fit. Relative to international SG-derived weights, the range of utility weights was larger with 5% of health states valued below zero.

Conclusion: s. DCEs can be used to investigate preferences for health profiles and to estimate utility weights for multi-attribute utility instruments. Australian cost-utility analyses can now use domestic SF-6D weights. The comparability of DCE results to those using other elicitation methods for estimating utility weights for quality-adjusted life-year calculations should be further investigated.

Keywords: Australia; SF-6D; cost-utility analysis; discrete choice experiment; economic evaluation; quality of life valuation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Algorithms
  • Australia
  • Choice Behavior
  • Cost-Benefit Analysis
  • Decision Support Techniques*
  • Female
  • Health Status*
  • Humans
  • Life Expectancy
  • Male
  • Mental Health*
  • Middle Aged
  • Quality of Life / psychology*
  • Quality-Adjusted Life Years
  • Socioeconomic Factors
  • Young Adult