Comparative Effectiveness And Cost-Effectiveness Analyses Frequently Agree On Value
Bibliographic record
Abstract
The Patient-Centered Outcomes Research Institute, known as PCORI, was established by Congress as part of the Affordable Care Act (ACA) to promote evidence-based treatment. Provisions of the ACA prohibit the use of a cost-effectiveness analysis threshold and quality-adjusted life-years (QALYs) in PCORI comparative effectiveness studies, which has been understood as a prohibition on support for PCORI's conducting conventional cost-effectiveness analyses. This constraint complicates evidence-based choices where incremental improvements in outcomes are achieved at increased costs of care. How frequently this limitation inhibits efficient cost containment, also a goal of the ACA, depends on how often more effective treatment is not cost-effective relative to less effective treatment. We examined the largest database of studies of comparisons of effectiveness and cost-effectiveness to see how often there is disagreement between the more effective treatment and the cost-effective treatment, for various thresholds that may define good value. We found that under the benchmark assumption, disagreement between the two types of analyses occurs in 19 percent of cases. Disagreement is more likely to occur if a treatment intervention is musculoskeletal and less likely to occur if it is surgical or involves secondary prevention, or if the study was funded by a pharmaceutical company.
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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 | 0.403 | 0.708 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".