A shortcut to mean-based time tradeoff tariffs for the EQ-5D?
ABSTRACT EQ-5D valuation studies are usually performed using the time tradeoff (TTO) method, which is costly and time consuming. We focused on 2 properties that particularly characterize TTO: the initial choice task categorizing health states as better than death (BTD), worse than death (WTD), or equal to death (ETD), and unwillingness to trade (UTT) lifetime to improve health. The aim of this study was to estimate the value of the information to be gained from continuing the conventional TTO tasks beyond the initial question and the extent to which mean-based EQ-5D tariff values could be predicted through a simplified method of categorizing health states into BTD, WTD, ETD, and UTT.
We used data from the UK EQ-5D valuation study (n = 2997). We designed an abbreviated system with only 4 values (collapsed TTO [cTTO]) based on the 4 response categories and assigned values as follows: WTD = -.5, ETD = 0, BTD = .5, and UTT = 1. Based on the mean cTTO scores for the valued health states, we created a regression-based cTTO tariff, which was compared with the conventional (full) TTO tariff (fTTO) by regressing 1) the fTTO means on cTTO means and 2) the fTTO tariff on the cTTO tariff.
WTD values were unrelated to health state severity. Correlation between the means of fTTO and means of cTTO was >.999, and tariff values from fTTO correlated with tariff values from cTTO at r > .999.
Once respondents have classified health states as UTT, BTD, ETD, or WTD, the TTO procedure adds little further information to the tariff values. The WTD task fails to discriminate between good and bad health states. TTO valuation could likely be simplified using cTTO.