Challenges to time trade-off utility assessment methods: when should you consider alternative approaches?
Bibliographic record
Abstract
In recent years, the time trade-off (TTO) method, most commonly with a 10-year time horizon, has been the most frequently used approach for direct health state utility assessment, likely due to National Institute for Health and Care Excellence (NICE) preference for comparability with the EQ-5D, which has a utility scoring algorithm derived via this method. Although comparability to previous utility studies is important, there are situations when the TTO method may not be appropriate. The purpose of the current review is to highlight challenges to the TTO method. Five challenges to the TTO method are discussed: mild health states, small differences among health states, temporary health states, pediatric health states, and assessment of samples with particular characteristics. Some of these challenges are associated with the 10-year time horizon, while other situations may raise issues for TTO methods regardless of the time horizon. Alternative approaches for valuing health states are suggested.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.256 | 0.464 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".