Onwards and Upwards: A Systematic Survey of Economic Evaluation Methods in Oncology
Notice bibliographique
Résumé
INTRODUCTION: The type of methods used in economic evaluations of health technology can lead to results that may influence decisions. Despite the potential impact on decision making, there is very little documentation of methods used in economic evaluation in oncology pertaining to key assumptions and extrapolation methods of survival benefits, especially in terms of survival analysis techniques and methods for extrapolation. OBJECTIVES: The primary objectives of this study were to identify, examine, and describe the methods used in economic evaluations in oncology over a 10-year period, while secondary objectives included examining the use of identified methods across different geographic regions. METHODS: A systematic search of the published oncology literature was conducted to identify economic evaluations of advanced or metastatic cancers published between 2010 and 2019 using the PUBMED, Ovid MEDLINE, and EMBASE databases. A random sample was taken, and information on type of study, data source, modeling techniques, and survival analysis methods were abstracted and descriptively summarized. RESULTS: A total of 8481 abstracts were identified and 76 economic evaluations were abstracted and assessed. Most identified studies were from North America (38%), East Asia (21%), continental Europe (18%), or the UK (16%), and most commonly focused on lung cancer (18%), colorectal cancer (16%), or breast cancer (13%). A large majority of studies were based on data from randomized controlled trials (82%), utilized a cost-utility approach (82%), and took a public healthcare system perspective (83%). Common model structures included Markov (49%) and partitioned survival (17%). Fitted parametric curves were the most commonly used extrapolation method (89%) for overall survival and most often utilized the Weibull distribution (64%). Secondary assessments showed modest regional variation in the use of identified methods, including the use of fitted parametric curves, testing of the proportional hazards assumption, and validation of results. CONCLUSION: A majority of papers in the study sample reported basic characteristics of study type, data source used, modeling techniques, and utilization of survival analysis methods. However, greater detail in reporting extrapolation methods, statistical analyses, and validation of results could be potential improvements, especially across regions, in order to support greater consistency in decision making. Future research could document the diffusion of novel modeling techniques into economic evaluation.
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 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 | 0,099 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 tête enseignante, 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 ».