Discrete choice modeling for the quantification of health states: the case of the EQ-5D

Probabilistic models have been developed to establish the relative merit of subjective phenomena by means of specific judgmental tasks involving discrete choices (DCs). The attractiveness of these DC models is that they are embedded in a strong theoretical measurement framework and are based on relatively simple judgmental tasks. The aim of our study was to determine whether the values derived from a DC experiment are comparable to those obtained using other valuation techniques, in particular the time trade-off (TTO).

Two hundred nine students completed several tasks in which we collected DC, rank, visual analog scale, and TTO responses. DC data were also collected in a general population sample (N=444). The DC experiment was designed using a Bayesian approach, and involved 60 choices between two health states and a comparison of all health states to being dead. The DC data were analyzed using a conditional logit and a rank-ordered logit model, relying, respectively, on TTO values and the value for being dead to anchor the DC-derived values to the 0 to 1 quality-adjusted life-year (QALY) scale.

Although modeled DC data broadly replicated the pattern found in TTO responses, the DC consistently produced higher values. The two methods for anchoring DC-derived values on the QALY scale produced similar results.

On the basis of the high level of comparability between DC-derived values and TTO values, future valuation studies based on a combination of these two techniques may be considered. The results further suggest that DC can potentially be used as a substitute for TTO.