Examining Trends in Cost and Clinical Benefit of Novel Anticancer Drugs Over Time
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine if clinical benefits of novel anticancer drugs, measured by the ASCO Value Framework and European Society of Medical Oncology (ESMO) Magnitude of Clinical Benefit Scale, have increased over time in parallel with increasing costs. METHODS: Anticancer drugs from phase III randomized controlled trials cited for clinical efficacy evidence in drug approvals between January 2006 to December 2015 were identified and scored using both frameworks. For each drug, the monthly price and incremental anticancer drug costs were calculated. Relationships between cost and year of approval were examined using generalized linear regressions models. Ordinary least square models were used to evaluate relationships between ASCO and ESMO scores and year of approval. Spearman correlation coefficients between costs and clinical benefit scores were calculated. RESULTS: In total, 42 randomized controlled trials were included. Both monthly prices and incremental anticancer drug costs were significantly associated with year of approval and showed an average annual increase of 9% and 21%, respectively. The predicted mean incremental anticancer drug cost increased from $30,447 in 2006 to $161,141 in 2015 (greater than five-fold increase). Both ASCO and ESMO scores were not statistically associated with year of approval or correlated with monthly prices or incremental anticancer drug costs. CONCLUSION: Over the past decade, costs of novel oncology drugs have increased, while clinical benefits of these medications have not experienced a proportional positive change. The incremental anticancer drug costs have increased at a much greater rate than monthly prices, indicating that the increase in anticancer drug costs may be higher than commonly reported.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".