Revisiting Indirect Health Preference Elicitation As A Base Case
Bibliographic record
Abstract
In the context of constrained health budgets, cost-effectiveness of health technologies has become an important concern. The strengths of this approach include its ability to explicitly model assumptions of what policy makers value when making a decision, to contextualize decisions in terms of absolute versus relative effects, and to use results to inform efficient use of research resources. A key aspect of many cost-effectiveness models is the quality adjusted life year (QALY), which allows analysts to capture the value of reduced mortality and improved quality of life simultaneously. Measurement of QALYs requires the determination of a quality weight which is bound at negative infinity (indicating death or health states worse than death) and one (indicating perfect health). While these weights can be elicited directly from patients through a standard gamble or time trade off, most national bodies recommend that preference for health states be determined by the general public. This recommendation comes despite the empirical evidence that these indirect health state valuations often differ in magnitude, and possibly in direction, to those directly elicited from patients. In this abstract, we assume that the purpose of cost-effectiveness analysis is to maximize population health and argue that recommendations to use indirect preference elicitation render this goal impossible to achieve. We use examples from the published literature to create two scenarios which illustrate how indirect preferences may be preferred for questions of prevention, but may lead to unjust and inefficient resource allocation that will meaningfully decrease population health when evaluating interventions to improve of cure disease. We argue that methods guidelines for cost-effectiveness analysis of health technologies ought to recommend that the source of health preferences match the population that will be most directly affected by the decision problem.
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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 | 0.055 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 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".