Disparities in Phase II and III Clinical Trial Enrollment for Hematologic Malignancies
Notice bibliographique
Résumé
Introduction Despite recent therapeutic advancement in treating hematologic malignancies (HM), disparities persist in health outcomes across age, sex, race, and ethnicity. Ensuring representative enrollment in clinical trials (CTs) is critical for improving accrual rates, understanding the heterogeneity of treatment effects, and increasing access to novel therapies. Disparities in CT enrollment for FDA-approved HM therapies have been reported, but comprehensive data on disparities across all HM CTs, regardless of regulatory status, are limited. We comprehensively analyzed disparities in the demographic characteristics of patients enrolled in CTs for lymphoma, leukemia, and multiple myeloma (MM). Methods We conducted a systematic search of all US-based phase II and III HM CTs registered in ClinicalTrials.gov from January 1, 2000, to December 31, 2023, that had completed accrual. Data abstraction captured trial characteristics (such as funding source) and demographic information (age, sex, race, and ethnicity). Minority enrollment was compared to age-adjusted population-based incidence statistics for HM from the Surveillance, Epidemiology, and End Results (SEER), which provided demographic subgroup proportions for sex, race (White, Black, Asian or Pacific Islander [A/PI], Native American/Alaska Native [NA/AN]), and ethnicity (Hispanic and non-Hispanic). Descriptive statistics were used to summarize the collected data across subgroups. Chi-square testing was used to assess the representativeness of CT enrollment with SEER proportions. Results A total of 1,286 CTs were extracted, comprising 583 CTs for lymphoma (Hodgkin lymphoma [HL], non-Hodgkin lymphoma [NHL], chronic lymphocytic leukemia [CLL]), 440 for leukemia (including acute myeloid leukemia [AML], acute lymphoblastic leukemia [ALL], and myeloproliferative neoplasms [MPN] including chronic myelogenous leukemia and myelodysplasia, and 42 for MM). The majority (87%) were phase II CTs and 369 (29%) received NIH funding. Regarding intervention type, 34% of trials involved novel therapies, 30% evaluated multiagent chemotherapy, and other therapies evaluated included single agent chemotherapy, monoclonal antibodies, and antibody-drug conjugates. The CTs included 153,074 participants, for which race was reported for 57% and ethnicity for 39%. Among those with available data (N=37,503), 32,646 (85.4%) were White, 7.7% A/PI, 6.3% Black, and 0.3% NA/AN. Most CT participants (87.9%) identified as non-Hispanic. Compared to the SEER population across all HM subtypes (10.3%), CTs underrepresented Black patients (6.3%; NIH funded CTs 8.9%; non-NIH funded CTs 5.8%). Notably, this disparity was significant for lymphoid malignancies (HL 10.3% vs. 12.3%, p<0.0001; NHL 4.5% vs. 8.1%, p<0.0001; CLL 3.8% vs. 6.9%, p<0.0001), myeloid malignancies [AML 6.6% vs. 9.5%, p<0.0001; MPN 4.9% vs. 10.1%, p<0.0001), and most notably for MM (7.1% vs. 20%, p<0.0001). Hispanic participants were underrepresented for HL (15.9% vs. 18%, p<0.0001), NHL (6.7% vs. 13.0%, p<0.0001), MM (7.2% vs. 12.7%, p<0.0001), CLL (4.0% vs. 5.6%, p<0.0001), and ALL (21% vs. 36.9%, p<0.0001). Hispanic participation was higher in NIH funded CTs (12.1%) compared to non-NIH funded CTs (8.6%). Conversely, CTs enrolled higher proportions of A/PI compared to the general population proportions for NHL (7.9% vs. 5.2%, p<0.0001), AML (8.9% vs. 5.8%, p=0.001), and MM (7.2% vs. 4.3%, p<0.0001). With the exception of CML, females were underrepresented across many HM CTs compared to SEER, including in lymphoma (41% vs. 45%, p<0.0001) and CLL (35.1% vs. 40.2%, p<0.0001). Small, but statistically significant differences were also observed for MM (43.5% vs. 45.1%, p<0.0001), and acute leukemias (44.3% vs. 45.6%, p<0.0001). Conclusion Race and ethnicity were reported for only about half of all participants, indicating a substantial gap in knowledge of representation in HM CTs. Our analysis revealed substantial underrepresentation of racial and ethnic minorities in CTs, with notable disparities for Black patients in MM CTs, and Hispanic representation for NHL and ALL CTs. Instances of over-representation were noted for A/PI populations, which may be influenced by geographical enrollment patterns such as international participation. Efforts to address these gaps are essential to ensure that therapy advancements benefit all patient populations equitably.
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 | 0,058 | 0,165 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,007 | 0,016 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 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) | 0,005 | 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 ».