Abstract P3-17-01: Geographic variation in adverse event reporting patterns in breast cancer clinical trials
Notice bibliographique
Résumé
Abstract Introduction: Adverse event (AE) reporting in clinical trials (CT) informs the safety of investigational products. Once approved, safety information in the monograph and prescribing information mainly derive from CT data. Some studies have shown geographic variations in the AE reporting patterns in multinational CT; none of them assessed this variation in cancer CT. We conducted a study to analyze the geographic AE reporting patterns in two breast cancer (BC) CT conducted by Translational Research in Oncology (TRIO). Objective: To perform a quantitative and qualitative comparison of non-serious AE (NSAE) and serious AE (SAE) reporting patterns between several geographic regions, in breast cancer CT conducted by TRIO. Methodology: We retrospectively analyzed aggregated NSAE/SAE data (as reported by investigators) from all patients randomized in two completed phase 3, multinational CT of anticancer therapies in advanced BC. Participating countries were grouped in 7 regions according to their geographic location (East Asia, Eastern Europe, Latin America and Caribbean, Middle East and Africa, Non-Eastern Europe, North America, Oceania). Regions were kept masked and numbered from 1 thru 7. AE data were extracted from the clinical data bases. For each region we calculated the mean number of NSAE and SAE per patient (pt), the mean number of NSAE and SAE per cycle/per pt, and the percentage (%) of pt experiencing selected AE (fatigue, febrile neutropenia and emesis). Comparisons between regions were done using unequal variance t-test and Fisher´s exact test. Results: 1,863 patients from 35 countries and 310 sites were included. Mean number of pt per region was 331. We found significant variation in the number of NSAE/SAE reported across several regions. Two regions (1 and 6) reported the highest mean number of AE while region 4 the lowest rates. The mean number of NSAE reported in region 4 is approximately 3-fold lower than regions 1 and 6 (mean NSAE 22.8 [region 1] vs. 7.9 [region 4]; p <.0001; mean NSAE cycle/pt 9.7 [region 1] vs. 3.2 [region 4]; p <.0001). Region 4 reported 8-fold lower rates of SAE than region 1 (mean number SAE 0.1 vs. 0.8, p<.0001) (Table 1). % of pt experiencing AE fatigue, febrile neutropenia and nausea/vomiting also varied significantly across regions, especially between regions 1 and 4 (Table 2). NSAE and SAE reporting in selected regions (regions 1 and 4) Region 1 (mean)Region 4 (mean)p-valueNSAE per pt22.87.9<.0001SAE per pt0.80.1<.0001NSAE per cycle/per pt9.73.2<.0001SAE per cycle/per pt0.10.03<.0001 % of pt experiencing selected AE in selected regions (regions 1 and 4) Region 1Region 4p-valueFatigue84.1 %8.5 %<.0001Febrile Neutropenia5.5 %2.1 %0.02Nausea and Vomiting61.0 %18.8 %<.0001 Conclusion: NSAE and SAE reporting patterns vary markedly by geographic region and one region appears to systematically under report both NSAE and SAE. These data warrant confirmation, and if confirmed, may provide an important caveat on the interpretation of reported study safety data. Citation Format: González V, Machado A, Fung H, Spera G, Meyer C, Millán P, Mackey JR, Fresco R. Geographic variation in adverse event reporting patterns in breast cancer clinical trials [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P3-17-01.
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 | 0,073 | 0,217 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,012 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».