A Comparative Analysis of Models Used to Evaluate the Cost-Effectiveness of Dabigatran Versus Warfarin for the Prevention of Stroke in Atrial Fibrillation
Notice bibliographique
Résumé
BACKGROUND: A number of models exploring the cost-effectiveness of dabigatran versus warfarin for stroke prevention in atrial fibrillation have been published. These studies found dabigatran was generally cost-effective, considering well-accepted willingness-to-pay thresholds, but estimates of the incremental cost-effectiveness ratios (ICERs) varied, even in the same setting. The objective of this study was to compare the findings of the published economic models and identify key model features accounting for differences. METHODS: All aspects of the economic evaluations were reviewed: model approach, inputs, and assumptions. A previously published model served as the reference model for comparisons of the selected studies in the US and UK settings. The reference model was adapted, wherever possible, using the inputs and key assumptions from each of the other published studies to determine if results could be reproduced in the reference model. Incremental total costs, incremental quality-adjusted life years (QALYs), and ICERs (cost per QALY) were compared between each study and the corresponding adapted reference model. The impact of each modified variable or assumption was tracked separately. RESULTS: The selected studies were in the US setting (2), the Canadian setting (1), and the UK setting (2). All models used the Randomized Evaluation of Long-Term Anticoagulation study (RE-LY) as the main source for clinical inputs, and all used a Markov modelling approach, except one that used discrete event simulation. The reference model had been published in the Canadian and UK settings. In the UK setting, the reference model reported an ICER of UK£4,831, whereas the other UK-based analysis reported an ICER of UK£23,082. When the reference model was modified to use the same population characteristics, cost inputs, and utility inputs, it reproduced the results of the other model (ICER UK£25,518) reasonably well. Key reasons for the different results between the two models were the assumptions on the event utility decrement and costs associated with intracranial haemorrhage, as well as the costs of warfarin monitoring and disability following events. In the US setting, the reference model produced an ICER similar to the ICER from one of the US models (US$15,115/QALY versus US$12,386/QALY, respectively) when modelling assumptions and input values were transferred into the reference model. Key differences in results could be explained by the population characteristics (age and baseline stroke risk), utility assigned to events and specific treatments, adjustment of stroke and intracranial haemorrhage risk over time, and treatment discontinuation and switching. The reference model was able to replicate the QALY results, but not the cost results, reported by the other US cost-effectiveness analysis. The parameters driving the QALY results were utility values by disability levels as well as utilities assigned to specific treatments, and event and background mortality rates. CONCLUSIONS: Despite differences in model designs and structures, it was mostly possible to replicate the results published by different authors and identify variables responsible for differences between ICERs using a reference model approach. This enables a better interpretation of published findings by focusing attention on the assumptions underlying the key model features accounting for differences.
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 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 | 0,038 | 0,105 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,009 |
| Bibliométrie | 0,006 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,003 | 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 ».