A Time-Trend Economic Analysis of Cancer Drug Trials
Notice bibliographique
Résumé
BACKGROUND: Scientific advances have led to the discovery of novel treatments with high prices. The cost to publicly fund high-cost drugs may threaten the sustainability of drug budgets in different health care systems. In oncology, there are concerns that health-benefit gains are diminishing over time and that the economic evidence to support funding decisions is too limited. METHODS: To assess the additional costs and benefits gained from oncology drugs over time, we used treatment protocols and efficacy results from U.S. Food and Drug Administration records to calculate cost-effectiveness ratios for drugs approved to treat first- and second-line metastatic or advanced breast, colorectal, and non-small cell lung cancer during the years 1994-2013. We assessed reimbursement recommendations reached by health technology assessment agencies in the U.K., Australia, and Canada. RESULTS: Cost-effectiveness ratios were calculated for 50 drugs approved by the U.S. regulator. The more recent approvals were often based on surrogate efficacy outcomes and had extremely high costs, often triple the costs of drugs approved in previous years. Over time, the effectiveness gains have increased for some cancer indications; however, for other indications (non-small cell lung and second-line colorectal cancer), the magnitude of gains in effectiveness decreased. Reimbursement recommendations for drugs with the highest cost-effectiveness ratios were the most inconsistent. CONCLUSION: Evaluation of the clinical benefits that oncology drugs offer as a function of their cost has become highly complex, and for some clinical indications, health benefits are diminishing over time. There is an urgent need for better economic evidence from oncology drug trials and systematic processes to inform funding decisions. IMPLICATIONS FOR PRACTICE: High-cost oncology drugs may threaten the ability of health care systems to provide access to promising new drugs for patients. In order to make better drug-funding decisions and enable equitable access to breakthrough treatments, discussions in the oncology community should include economic evidence. This study summarizes the extra benefits and costs of newly approved drugs from pivotal trials during the postgenomic era of drug discovery. The reader will gain an appreciation of the need for economic evidence to make better drug-reimbursement decisions and the dynamics at play in today's oncology drug market.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,050 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».