Censoring time trade-off values at 0 versus at -1: how does the assumption for worse-than-dead TTO values affect the modelling of EQ-5D-5L valuation data?

Published:November 10, 2022DOI:
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      • 1.
        Valuing worse than dead health states have been proven as a challenging task for general public. Studies reported the negative values from EQ-5D valuation studies lack sensitivity.
      • 2.
        Our study assumed the negative values of EQ-5D-5L valuation studies are less valid and by censoring the negative values at 0, the model may produce better modelling results.
      • 3.
        By comparing the modelling results between the censoring at 0 tobit model and the censoring at -1 tobit model in five EQ-5D-5L valuation data, we found censoring at 0 improved model coefficients’ significance and consistency, but compressed the predicted value range.



      A recent study found that negative utility values elicited using composite TTO were barely associated with the severity of EQ-5D-5L health states, suggesting poor discriminative ability. Assuming negative values provide limited information, this study aimed to explore the usefulness of censoring negative TTO values at 0 in modelling EQ-5D-5L valuation data.


      We analyzed EQ-5D-5L valuation data from China, the Netherlands, Canada, Singapore, and Thailand. For each dataset, we estimated value sets using two Tobit models, one left-censored at -1 (current practice) and one left-censored at 0 (our proposed method) and compared the model performances. We hypothesized that censoring at 0 and censoring at -1 would produce similar values, though on slightly different scales.


      When censoring at 0, logical inconsistencies and statistical significance were improved but the value range was compressed. In the CALE model, the three level parameters were similar between the models censored at 0 and -1, but the rank order of some dimension parameters was altered. Health state values predicted by the two censoring models approximated a perfect agreement after rescaling.


      Censoring TTO values at 0 improved model estimation and fit but produced higher utility values compared to models censoring at -1. Investigators of future EQ-5D value set studies using the cTTO method are advised to examine the validity of negative TTO values before choosing modelling strategies.
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