Using Quality-Adjusted Life-Years in Cost-Effectiveness Analyses: Do Not Throw Out the Baby or the Bathwater
Bibliographic record
Abstract
Weneed ametric to understand the value of cancer treatments—this issue is not under debate. In recent months, different professional societies have produced tools to attempt to assess value, but they have avoided the use of cost-effectiveness studies. The science of cost effectiveness has come under attack in recent years, with specific questions regarding the use of qualityadjusted life-years (QALYs). Although costeffectiveness techniques are used in many countries to make decisions regarding coverage of health care interventions, some stakeholders have challenged the appropriateness and accuracy of this science. Riesco-Martinez et al have demonstrated how to perform a well-balanced costeffectiveness study analyzing different treatment sequences for metastatic colon cancer. They did this from the perspective of a Canadian payer, and in their analysis they included the use of QALYs. They should be commended for this analysis,andtheresultsmaybeusedtoguide coverage decisions in Canada. However, in many parts of the world, particularly the United States, there is major opposition to the use of cost-effectiveness analyses, particularly the use of QALYs. When the United States Congress developed the Patient-Centered Outcomes Research Institute, it explicitly forbade the use of QALYS in cost-effectiveness research. Whydo somany stakeholders involved in health care have such visceral opposition to cost-effectiveness research and the use of QALYs? To understand this, we must first understand the definition of a QALY?
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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 | 0.272 | 0.568 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".