Whether and How Disutilities of Adverse Events were Used in the Economic Evaluation of Drug Therapy for Cancer Treatment
Notice bibliographique
Résumé
BACKGROUND: The disutilities of adverse events (AEs) are important inputs for cost-utility analysis (CUA), reflecting the impacts of AEs on health outcomes. Health technology assessment institutions and scholars have proposed recommendations for applying disutility values in economic evaluations. OBJECTIVES: This study aimed to identify the current use of disutilities of AEs as model parameters in the CUA of cancer drug therapy and to compare the discrepancies between the use of disutilities and published recommendations. METHODS: A systematic search was conducted on the PubMed, Web of Science, and Cochrane Library databases, as well as the official websites of the National Institute for Health and Care Research (NIHR), the Institute for Clinical and Economic Review (ICER), the Institute for Quality and Efficiency in Health Care (IQWiG), the Canadian Agency for Drugs and Technologies in Health (CADTH), and the Centre for Reviews and Dissemination (CRD) for CUAs of drug therapy for cancer published in English from January 2019 to April 2022. Information about the use of disutilities of AEs (whether and how disutilities were used, or why they were not used) in selected studies was extracted and compared with published recommendations. Descriptive analyses were used to summarize the results. RESULTS: A total of 467 CUAs were included, 54% (254/467) of which included disutilities of AEs in their model. The proportion that included these disutilities increased from 2019 to 2021, ranging from 47% (51/107) to 61% (116/190). Only 6% (15/254) of the CUAs using disutilities of AEs considered all five recommendations about the justification for inclusion and exclusion, description of values and sources, grades of AEs, calculation, and uncertainty analyses. Only 15% (72/467) provided a clear justification for inclusion and exclusion of disutilities of AEs, and 7% (17/254) did not provide values or sources. In total, 69% (175/254) of the analyses focused on AEs of grade 3 or greater, and 11% (28/254) applied utility decrements for grades 1 and 2. Disutilities of AEs were generally calculated using the incidence rates, which were clearly stated in 49% (65/132) of the analyses. Uncertainty analyses were conducted in 84% (214/254) of the CUAs. CONCLUSIONS: The current use of disutilities of AEs in CUAs shows some discrepancies with recommendations proposed in the literature. One is that detailed information about the use of disutilities of AEs was not reported and the other is that essential methods to analyze the impact of AEs on quality-adjusted life-years were not thoroughly conducted. Therefore, it is suggested that researchers should attach importance to the impact of AEs on health-related quality of life. Furthermore, an application process was developed for the disutilities of AEs to remind and guide researchers to correctly use the disutilities of AEs as parameters in the decision-analytic model.
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 | 0,019 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 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 ».