Cost-Utility Analysis Using EQ-5D-5L Data: Does How the Utilities Are Derived Matter?
Bibliographic record
Abstract
OBJECTIVES: To explore how the use of EQ-5D-5L value set and crosswalk from EQ-5D-5L to EQ-5D-3L (and use of 3L value set) would affect cost-effectiveness analysis results for England and six other countries (Canada, the Netherlands, China, Japan, South Korea, and Singapore). METHODS: Individual-level utilities derived from primary 5L data using both value set (5L) and crosswalk (c5L) approaches were applied to three Markov models assessing the cost-effectiveness of hemodialysis (HD) and peritoneal dialysis (PD) for end-stage renal disease (ESRD) patients to estimate incremental quality-adjusted life years (QALYs). The mathematic functions between incremental QALY and utility were derived. RESULTS: 5L- and c5L-based incremental QALYs were similar in the model for non-diabetic patients (range: 1.910-2.149, 1.922-2.121). 5L tends to generate more incremental QALYs than c5L in the model for diabetic patients (range: 1.454-1.633, 1.365-1.568) but fewer incremental QALYs in the model for all ESRD patients (range: 0.290-0.480, 0.315-0.493). In all models, 5L (c5L) generated more incremental QALYs when Chinese (South Korean) value sets were used. The largest and smallest differences in 5L- and c5L-based incremental QALYs were observed when Chinese and Dutch value sets were used. Incremental QALYs was a positive linear function of both utility of PD and difference in utilities of HD and PD. CONCLUSIONS: The value set and crosswalk approaches may not be used interchangeably in economic evaluation when EQ-5D-5L data are used to estimate utilities. Results of cost-effectiveness analysis using Markov models may be affected by both absolute utilities and their differences.
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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 | 0.029 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".