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 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 | 0.037 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".