Trends in the application of dynamic allocation methods in multiarm cancer clinical trials
Notice bibliographique
Résumé
6550 Background: In multiarm oncology clinical trials there is always a risk that the treatment groups could be imbalanced for a prognostic factor, potentially biasing treatment comparisons and compromising the validity of the results. Stratified randomization (SR) is frequently used to reduce imbalances. Dynamic allocation (DA), also known as minimization, is a controversial and largely nonrandom method of treatment allocation that can incorporate more prognostic factors than traditional SR methods. Proponents argue that results from clinical trials implementing DA are potentially more credible, while opponents claim there is little added benefit, increased complexity, and the statistical properties are not fully understood. We reviewed multi-arm cancer trials published between 1995–2005 to describe trends in the application of DA methods. Methods: 476 clinical trials with at least 100 patients in each arm, published in 13 major journals were reviewed. Manuscripts were grouped by impact factor (IF) of the publishing journal into low (<10), medium (10–20) and high (20+) categories. Trial-specific factors associated with publication were collected along with details of allocation method. Results: 112 (24%) trials described using DA for assigning patients to treatment. 79% of trials employing DA methods included 3 or more stratification factors, compared with only 36% of other trials (p < 0.001). Reported use of DA was similar between industry and non-industry sponsored studies (p = 0.85) and by geographical region (p = 0.73). Of 364 trials which did not describe using DA, 103 (28%) reported using SR, but a statement describing stratification on at least one factor was included in 291 (80%) trials. A trend was observed that reported use of both DA (p = 0.072) and SR (p = 0.067) methods increased over time. DA was associated with publication in higher IF journals univariately (OR = 1.67, 95% CI 1.11–2.52, p=0.014) and after adjusting for identified prognostic factors (OR = 1.70, 95% CI 1.06–2.73, p = 0.028). No association between SR and higher IF journal publication was observed (p = 0.52 univariately and p = 0.44 adjusting for other factors). Conclusions: DA is frequently used in cancer clinical trials. Reported use of DA, but not SR, is associated with publication in high IF journals. No significant financial relationships to disclose.
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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 | 0,294 | 0,554 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».