Is health-related quality of life (HRQoL) reporting keeping pace with new drug approvals in hematology and oncology: A five-year analysis of 245 drug approvals.
Notice bibliographique
Résumé
6519 Background: HRQoL data in cancer clinical trials can inform tolerability of new drugs, facilitate informed decision-making, and influence health care and policy decisions, but are frequently underreported. We reviewed all registration trials that informed Food and Drug Administration (FDA) approval between 2015-2020 for latency and quality of HRQoL reporting. Methods: HRQoL data for each clinical trial associated with FDA drug approval between 7/2015-5/2020 was collected retrospectively from multiple sources including the FDA and clinicaltrials.gov website, conference abstracts, and journal manuscripts. The aim of the study was to analyze the proportion of trials reporting HRQoL, quality of HRQoL data, latency between FDA approval and first reporting of HRQoL data, and association between changes in HRQOL and overall survival (OS) and progression-free survival (PFS) outcomes. Results: Of the 259 trials involving 245 drug approvals, majority involved solid tumors (61.4%), were phase III (59.1%), and led to approval based on a non-PFS/OS endpoint (52.9%). HRQoL was a pre-specified endpoint in 55.2% and reported in 49.8% trials. HRQoL data was published by the time of FDA approval in only 41.8% cases, 24.8% reported HRQoL data > 12 mo after approval. Further, among trials reporting HRQoL (n = 129), HRQoL data was first reported in the primary paper in only 34.1%, and either in an ancillary paper in 41.9% or an ancillary abstract in 24% trials. Of the 129 trials with HRQoL data, an improvement in HRQoL was seen in 44.2%, no significant change in 41.9%, mixed results in 11.6%, and worsening in 2.3% of trials. Overall, by the time of FDA approval, OS and PFS data were reported in 59% (152/259) and 65% (168/259) trials respectively with an OS benefit seen in 23.9% (62/259) trials, and PFS benefit in 38.6% (100/259) trials. Of the 84 trials that led to FDA approvals based solely on response rate, HRQoL was reported in only 23.8% (n = 24) with a HRQoL benefit seen in only 9.5% (n = 8) trials. Trials reporting either no significant impact on HRQoL or a mixed impact on HRQoL reported median OS benefit of 4.6 months and 4.2 months respectively. In trials reporting HRQoL data > 6 mo from FDA approval, OS benefit of < 3 mo was seen in 17.8% (8/45) trials. No significant time trends were noted during the study period. Conclusions: There was significant underreporting of HRQoL outcomes in trials ( < 50%) associated with FDA drug approvals between 2015-2020 with majority of trials reporting HRQoL data in an ancillary paper/abstract, and at a much later time than the FDA approval. While it is widely accepted that timely dissemination of HRQoL data from cancer drug trials are vital for clinical and regulatory decisions, no improvement in reporting rates were noted over past 5 years. Only 10% of drugs approved on the basis of response rates showed improvement in HRQoL in the registration trials.
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 | 0,048 | 0,070 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,005 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».