Examining the relationship between cost of novel oncology drugs and their clinical benefit over time.
Bibliographic record
Abstract
6598 Background: The average launch price of oncology drugs has increased by 10% annually from 1995 to 2013. The purpose of this study was to determine if the clinical benefit of novel oncology drugs has increased proportionally over time and is correlated with launch price. Methods: Novel oncology drugs fromrandomized controlled trials (RCTs) cited for clinical efficacy evidence in drug approvals between January 2006 and August 2015 were identified. For each drug, only the first FDA approved indication was included. To determine clinical benefit, all included RCTs were scored using the ASCO Value Framework and the ESMO Magnitude of Clinical Benefit Scale. The launch price for the FDA approval year of each drug was extracted from RedBook. Each drug’s 28-day cost was determined using the dosage schedule outlined in the respective RCT and was adjusted to 2015 USD using the consumer price index. The relationships between 28-day drug cost and FDA approval year, and between incremental drug cost (difference in total drug cost between experimental and control arms accounting for treatment duration) and FDA approval year were examined using generalized linear regression models (gamma distribution and log link). Ordinary least square models were used to evaluate the relationship between ASCO/ESMO scores and FDA approval year. Spearman’s correlation coefficients between 28-day/incremental drug costs and ASCO/ESMO scores were also calculated. Results: Forty RCTs were included in this analysis. The 28-day drug cost was significantly associated with FDA approval year (p = 0.04), with an average increase of 8.5% per year. Incremental drug cost was also significantly associated with FDA approval year (p < 0.001) with an increase of 28.6% per year. The mean ASCO and ESMO scores were 26 and 3, respectively. Both scores were not statistically associated with FDA approval year (p = 0.73 and p = 0.86, respectively) and were also not correlated with 28-day or incremental drug costs (all rho < = 0.2). Conclusions: Novel oncology drugs are not priced according to their clinical benefit. The rising cost of novel oncology drugs over time is not associated with an increase in their clinical benefit, suggesting a decrease in their value over time.
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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 | 0.027 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".