Modifying the Composite Time Trade-Off Method to Improve Its Discriminatory Power

M Jakubczyk*, Stefan Lipman, Bram Roudijk, Richard Norman, E Pullenayegum, Yaling Yang, Ning Yan Gu, Elly Stolk

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Objectives: In cost-effectiveness analysis of health technologies, health state utilities are needed. They are often elicited with a
composite time trade-off (cTTO) method, particularly for the widely used EQ-5D-5L. Unfortunately, cTTO discriminatory
power is hindered by (1) respondents’ nontrading (NT) of time for quality, (2) censoring of utilities at 21, and (3) poor
correlation of negative utilities with state severity. We investigated whether modifying cTTO can mitigate these effects.
Methods: We interviewed online 478 students (February to April, 2021) who each valued the same 10 EQ-5D-5L health states
in 1 of 3 arms. Arm A used a standard cTTO, expanded with 2 questions to explore reasons for NT and censoring. Arms B and C
used a time trade-off with modified alternatives offered to overcome loss aversion, to unify the tasks for positive and negative
utilities, and to enable eliciting utilities , 21.
Results: In arms B and C, we observed less NT than in A (respectively, 4% and 4% vs 10%), more strictly negative utilities (38%
and 40% vs 25%), and more utilities # 21 (18% and 30% vs 10%). The average utility of state 55555 dropped to 22.15 and 22.52
from 20.53. Enabling finer trades in arm A reduced NT by 70%. Arms B and C yielded an intuitive association between
negative utilities and state severity. These arms were considered more difficult and resulted in more inconsistencies.
Conclusions: The discriminatory power of cTTO can be improved, but it may require increasing the difficulty of the task. The
standard cTTO may overestimate the utilities, especially of severe states.
Original languageEnglish
Number of pages12
JournalValue in Health
Publication statusE-pub ahead of print - 13 Oct 2022

Bibliographical note

Funding/Support: This work was supported by grant number EQ Project
20190070 from the EuroQol Research Foundation.


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