Examining the relationship between cost of novel oncology drugs and their clinical benefit over time.
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,099 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,005 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».