What price for a year of life? A survey of U.S. and Canadian oncologists
Bibliographic record
Abstract
6565 Background: New cancer drugs are increasingly expensive and raise difficult questions about the magnitude of therapeutic benefit needed to justify their incremental cost. In this context, it is unclear whether oncologists endorse standard thresholds of $50,000 to $100,000 per year of life. Methods: We surveyed 1,379 U.S. and 356 Canadian (Cdn) oncologists and asked how much longer a patient would need to survive metastatic cancer to justify the expense of a new treatment. To determine the stability of attitudes towards cost-effectiveness (CE) we randomized oncologists to receive two different versions of the scenario in which the price of the new treatment was varied (higher versus lower drug cost). In the U.S. survey, oncologists were also randomized to receive surveys in which we varied the provision of contextual information about the CE of several familiar interventions. Both U.S. and Cdn oncologists were asked to indicate what they “thought was ‘good value for money’ expressed as cost per life-year gained (LYG).” Results: Response rate was 57% in the U.S. and 48% in Canada. CE ratios implied by oncologists’ responses differed significantly between the groups randomized to the higher versus lower price of the hypothetical treatment (p < 0.001 U.S., p < 0.0001 Canada), but were independent of randomization to varying contextual information (p > 0.1). The median willingness to pay for a quality-adjusted year of life ranged from $150,000 (for oncologists considering the lower priced drug) to $250,000 (for those considering the more expensive drug) in both countries. Among those who considered the more expensive drug, 25% of respondents implicitly endorsed a CE ratio greater than $600,000 (U.S.) and $500,000 (Canada). In contrast, when asked directly to indicate CE ratios that were good value for the money outside of the clinical scenario, 70% (U.S.) and 64% (Canada) of respondents indicated values of less than $100,000 per LYG. Conclusions: Oncologists responding to our survey provided inconsistent views on how much benefit expensive new drugs should provide to be worthwhile. This suggests that means of eliciting input from physicians that reflect more stable attitudes need to be developed to appropriately inform decision-makers. No significant financial relationships to disclose.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".