Willingness-to-Pay the Banker vs. the Economist
Bibliographic record
Abstract
Rationale: In the incremental net benefits approach to economics evaluation, willingness-to-pay (lambda) is defined as the trade-off between costs and effects; however, this definition is not true to first principles of economics, namely, opportunity cost. Given that decision-makers are credited with providing services with good effectiveness and safety profiles while containing budgets within some reasonable boundaries, it seems reasonable that the opportunity cost of providing services with a certain cost-effectiveness profile would be the opportunity foregone to maintain extra budget reserves. In the language of economics, the marginal utility of extra budget reserve dollars must be balanced against marginal utility of extra health outcomes. Therefore from the perspective of an economist dedicated to first principles, lambda should reflect the trade-off between budget containment opportunities and health technology investments that deliver additional benefits (at additional cost) - in other words, the tradeoff between budget impact and cost-effectiveness. Objective: To determine lambda at different levels of budget impact. Methodology: The conjoint analysis compares three different options for hemodialysis: 1. nocturnal hemodialysis (usual care without drug supplementation) 2. nocturnal hemodialysis plus a drug that reduces the utilization of hemodialysis in a relatively short time period 3. nocturnal hemodialysis plus a drug that reduces the utilization of hemodialysis but at a slower rate than in option 2. Each treatment option has three attributes: 1. cost-effectiveness (CE): $/QALY; 2. budget impact (BI): expected percent of total drug budget, and; 3. expected change in remaining unexpended budget (UB). The following list presents the treatments and their associated attribute levels. A total of 24 scenarios is included in the survey instrument. Cost-Effectiveness, 4 levels (per QALY): 1. $50,000 vs. $20,000 vs. $40,000, 2. $50,000 vs.$50,000 vs. $75,000, 3. $50,000 vs.$60,000 vs. $100,000, 4. $50,000 vs.$90,000 vs. $180,000 Budget Impact, 2 levels (PMPM): 1. 0% vs. 1% vs. 0.5% , 2. 0% vs. 0.1% vs. 0.05% Unexpended budget, 3 levels: 1. 5% (no change) vs. 5% to 4% vs. 5% to 4.5%; 2. 1% (no change) vs. 1% to 0% vs. 1% to 0.5%; 3. 0.5% (no change) vs. 0.5% to 0.5% over budget vs. 0.5% to 0% A random sample of decision-makers across Canada (n=36) are to be used to elicit decision-maker preferences and dismissed members of a local jury pool are to be used to elicit preferences from a random sample of the population (n=500). Results: Preliminary results (n=24) indicate that threshold willingness-to-pay lies between $50,000 - $75,000 CDN per QALY so long as budget impact is below 1%. Above this amount, threshold CE falls to below $50,000/QALY. Only rarely does anyone suggest that additional funds should be requested to fund one of the new alternatives. Full results should be available during the summer of 2007. Conclusion: This paper helps to explain part of the observed variation in lambda among newly approved medical interventions.
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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 | 0.059 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".