A Direct Comparison Between Discrete Choice With Duration and Composite Time Trade-Off Methods: Do They Produce Similar Results?
Bibliographic record
Abstract
OBJECTIVES: Discrete choice experiments including a duration attribute (DCEd) represent a promising candidate method for valuing health-related quality-of-life instruments. However, it has not been established that DCEd can produce similar results as composite time trade-off (cTTO) or EuroQol Valuation Technology (EQ-VT) valuations of the EQ-5D-5L instrument. This study provides a direct comparison between cTTO and EQ-VT, and DCEd valuation methods. METHODS: An EQ-VT study was conducted in Trinidad and Tobago to value the EQ-5D-5L. 1079 respondents each completed 10 cTTO tasks and 12 discrete choice experiments tasks without a duration attribute. A separate sample of 970 respondents each completed 18 split-triplet DCEd tasks. Several regression models were applied to the EQ-VT data, and the DCEd data were analyzed using mixed logit models with an exponential discount rate. The estimated values were compared using scatterplots and Bland-Altman plots. RESULTS: The ordering of dimensions was identical in level 5 for cTTO/EQ-VT and DCEd models, with pain/discomfort being the most important dimension and usual activities being least important. cTTO/EQ-VT models produced a value for state 55555 ranging between -0.52 and -0.69, whereas this was -0.543 for the nonlinear mixed logit model for the DCEd data. Scatterplots and Bland-Altman plots suggested excellent agreement between cTTO/EQ-VT and DCEd-based estimates. CONCLUSIONS: CTTO/EQ-VT and DCEd valuations produce similar results when correcting DCEd for nonlinear time preferences. The ordering of importance of the dimensions and scale are identical, suggesting that the 2 methods measure the same construct and produce similar results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".