Potencial sesgo de patrocinio en los análisis coste-efectividad de intervenciones sanitarias: un análisis transversal
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between the funding source of cost-effectiveness analyses of healthcare interventions published in Spain and study conclusions. DESIGN: Descriptive cross-sectional study. LOCATION: Scientific literature databases (until December 2014). PARTICIPANTS (ANALYSIS UNITS): Cohort of cost-effectiveness analysis of healthcare interventions published in Spain between 1989-2014 (n=223) presenting quality-adjusted life years (QALYs) as the outcome measure. MAIN MEASUREMENTS: The relationship between qualitative conclusions of the studies and the type of funding source were established using Fisher's exact test in contingency tables. Distributions of the incremental cost-effectiveness ratios by source of funding in relation to hypothetical willingness to pay thresholds between €30,000-€50,000 per QALY were explored. RESULTS: A total of 136 (61.0%) studies were funded by industry. The industry-funded studies were less likely to report unfavorable or neutral conclusions than studies non-funded by industry (2.2% vs. 23.0%; P<.0001), largely driven by studies evaluating drugs (0.9% vs. 21.4%; P<.0001). The incremental cost-effectiveness ratios in studies funded by industry were more likely to be below the hypothetical willingness to pay threshold of €30,000 (73.8% vs. 56.3%; P<.0001) and €50,000 (89.4% vs. 68.2%; P<.0001) per QALY. CONCLUSIONS: This study reveals a potential sponsorship bias in cost-effectiveness analyses of healthcare interventions. Studies funded by industry could be favoring the efficiency profile of their products.
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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 | 0.125 | 0.218 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".