Disparities in Phase II and III Clinical Trial Enrollment for Hematologic Malignancies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.165 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".