Association between control group therapy and magnitude of clinical benefit of cancer drugs.
Notice bibliographique
Résumé
e18604 Background: Oncology societies have developed tools to quantify the magnitude of clinical benefit. These include the American Society of Clinical Oncology Value Framework (ASCO-VF) version 2, the European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS) version 1.1, the National Comprehensive Cancer Network (NCCN) Evidence Blocks and the ASCO Cancer Research Committee criteria (ASCO-CRC). Associations between the characteristics of approved drugs and magnitude of clinical benefit have been explored in depth. Here, we assess the association between characteristics of control group therapy and magnitude of clinical benefit. Methods: We searched Drugs@FDA to identify new solid tumor cancer drugs approved based on randomized trials (RCTs) between January 2012 and December 2021. Substantial clinical benefit was defined as: overall survival gain ≥ 2.5 months and progression-free survival gain ≥ 3 months for the ASCO-CRC (palliative setting only); ASCO-VF threshold score ≥45 (palliative and curative setting); NCCN Evidence Blocks threshold score ≥16 (palliative and curative setting); and grade A or B for trials of curative intent and 4 or 5 for those of non-curative intent using ESMO-MCBS. Associations between characteristics of control group therapy (e.g. type of active therapy, use of matched placebo and overlap between experimental and control therapy) and substantial clinical benefit scores were explored using logistic regression. Results: We identified 174 RCTs supporting the approval of 76 drugs for 164 indications. Of these, 47% (82/174) were placebo-controlled trials among which 42% (34/82) comprised active treatment with a matched placebo. Substantial clinical benefit was observed in 45%, 46%, 72% and 73% using the ESMO-MCBS, ASCO-VF, NCCN Evidence Blocks and ASCO-CRC, respectively. These low proportions resulted in an inability to fit multivariable models adequately. RCTs with a control group comprising of active treatment with a matched placebo were associated with significantly lower odds of substantial benefit with ESMO-MCBS (OR 0.27, P = .003) and ASCO-VF (OR 0.30, P = .008) but not with NCCN Evidence Blocks (OR 0.74, P = .55) or ASCO-CRC criteria (OR 1.36, P = .54). Similar results were observed when excluding trials in the curative setting. There was a non-significant association with higher odds of substantial benefit with ESMO-MCBS with trials in which the control group was chemotherapy (OR 1.97, P = .07). For ASCO-CRC a non-significant association in the opposite direction was observed (OR 0.40, P = .06). Conclusions: Clinical benefit scales can be sensitive to the type of control group therapy. RCTs with an active treatment and matched placebo in the control group were less likely to be scored as providing substantial clinical benefit using the ESMO-MCBS and the ASCO-VF scales. Control group therapy did not influence NCCN Evidence Blocks or ASCO-CRC scores.
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 | 0,053 | 0,159 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,008 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 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 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 ».