OBJECTIVES:

People rarely actually trade health; hence, health preferences are not well formed. I assess the possibility of using fuzzy numbers when eliciting the utilities in time trade-off (TTO) and estimating the dimension importance and value sets.

METHODS:

A modified-TTO survey was used. Respondents (184, a convenience sample) answered demography questions, self-rated own health, and answered ten TTO tasks. Apart from a standard valuation, the respondent provided ranges of equally/somewhat plausible answers (EPAS/SPAS), which define the (dis)utility as a trapezoidal fuzzy number. The length of EPAS/SPAS was compared with the standard error of (a crisp) mean (SEM). The determinants of EPAS length were identified. I built several models to identify dimensions impact on (dis)utility: (A, as a benchmark) crisp disutility-crisp parameters; (B) fuzzy disutility-crisp parameters, based on the directed Hausdorff distance; two fuzzy-fuzzy models: using the Hausdorff distance (C1) or modelling the middles and lengths of EPAS (C2). Value sets were constructed.

RESULTS:

The average length of EPAS varied between 0.063 (state 21111) and 0.137 (11113), 2–6 times the length of SEM. EPAS widens with usual activities (UA) and anxiety/depression. Derived variables (e.g. maximal level, misery index) improve the fit considerably, and were used in C2. When modelling disutility, models A and B produce similar results (with u(55555)≈-0.8), proving the impact of imprecision is little with crisp parameters assumed. In C1, the largest imprecision is associated with levels 3 of UA ([0.343;0.443]) and pain/discomfort ([0.423;0.498]). Counterintuitively, some parameters (e.g. for mobility) degenerate to zero-length intervals. C2 seems most favourable approach as the worsening in any dimension implies imprecision; e.g., u(55555)=[-0.828;-0.716].

CONCLUSIONS:

In eliciting utilities of health states, the imprecision (not decreasing with sample size) surpasses the stochastic uncertainty. Fuzzy methods allow inspection of mechanism behind imprecision and extrapolation onto value set. The inherent imprecision should be handled in decision making.