MétaCan
Menu
Retour à la cohorte
Enregistrement W2946884648 · doi:10.1002/cam4.2269

The impact of pricing strategy on the costs of oral anti‐cancer drugs

2019· article· en· W2946884648 sur OpenAlexaffabout
Judy Truong, Kelvin Chan, Helen Mai, Alexandra Chambers, Mona Sabharwal, Maureen Trudeau, Matthew C. Cheung

Notice bibliographique

RevueCancer Medicine · 2019
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueEconomic and Financial Impacts of Cancer
Établissements canadiensCanadian Agency for Drugs and Technologies in HealthCanadian Centre for Applied Research in Cancer ControlHealth Sciences CentrePublic Health OntarioUniversity of TorontoSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineCancerDrugCancer drugsPharmacologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: The soaring costs of anti-cancer drugs pose a threat to the sustainability of cancer care. The pricing strategy chosen by manufacturers can impact the costs of oral anti-cancer drugs during dose modifications, but this issue remains under-recognized in the literature. In general, with the flat pricing strategy, there is a single fixed price for each tablet regardless of dosage strength, whereas with linear pricing, the price of each tablet increases with its dose. We hypothesize that flat pricing will have increased drug costs compared to linear pricing during dose reductions since the cost remains fixed despite decreased dose requirements. This practice may have significant financial implications considering the high costs, extensive utilization, and frequent occurence of dose reductions with anti-cancer drugs. METHODS: Oral anti-cancer drugs reviewed by the pan-Canadian Oncology Drug Review program between 2011 and 2018 were identified. The cost per mg and cost per 28-day cycle were calculated for dose levels -2 to +2. The percent change in cost per mg and cost per cycle during dose modifications from the standard dose were calculated. We conducted Mann-Whitney U and Fisher-exact tests to compare the association between drug costs during dose reductions and pricing strategy. RESULTS: In this study, 30 oral anti-cancer drugs for use in 41 indications were analyzed; 44% of drugs used linear pricing and 56% used flat pricing. Dose reductions increased the mean cost per mg for drugs with linear pricing by 14.7% (range: 0%-50%) at dose level -1 and 17.2% (range: 0%-50%) at dose level -2 and flat pricing by 60.8% (range: 19%-100%) at dose level -1 and 99.1% (range: 0%-300%) at dose level -2. The cost per mg was significantly increased in drugs using flat pricing compared to linear pricing when dose reduction to either level -1 (P = 0.010) or level -2 (P = 0.006) occurred. The mean cost per cycle was decreased for drugs using linear pricing by 20.9% (range: -40% to 0%) at dose level -1 and 48.7% (range: -60% to -25%) at dose level -2 and flat pricing by 0.8% (range -6% to 0%) at dose level -1 and 11.0% (range: -50% to 100%) at dose level -2. The cost per cycle was significantly decreased in drugs with linear pricing compared to flat pricing when the standard dose is reduced to either dose level -1 (P = 0.005) or dose level -2 (P = 0.026). CONCLUSIONS: Overall, flat pricing had significantly greater costs compared to linear pricing during dose reductions of anti-cancer drugs. While there is a general expectation that the cost of drugs should decrease with dose reduction, drugs with flat pricing were generally found to have increased cost per mg and no change in the cost per cycle. The resultant increased spending on drug acquisition (despite purchasing lower doses) lead to financial wastage, which has significant implications on cost-effectiveness considerations and budgets. Future economic evaluations should take into consideration the hidden costs associated with dose reductions of flat priced drugs.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,468
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,037
Tête enseignante GPT0,301
Écart entre enseignants0,265 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations13
Publié2019
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueCancer MedicineMême sujetEconomic and Financial Impacts of CancerTravaux en français237 207