Representation of the Population in Need for Pivotal Clinical Trials in Lymphomas.
Mycal CaseyLorriane A OdhiamboNidhi AggarwalMahran ShoukierK M IslamJorge E CortesPublished in: Blood (2023)
Despite the advances in cancer outcomes, significant health disparities persist. Several new agents have been recently approved for treatment of lymphomas leading to improved outcomes. Extending the benefits of these new agents starts by adequate enrollment of all affected patient populations. To evaluate the extent to which randomized controlled trials (RCTs) match the demographic and geographic diversity of the population affected by lymphoma. Two Food and Drug Administration databases, ClinicalTrials.gov, and relevant primary manuscripts were reviewed for drug approval data and demographic representation in RCTs for classical Hodgkin Lymphoma (cHL) and non-Hodgkin's Lymphoma (NHL). Maps showing the distribution and frequency of trial participation relative to disease burden, insurance status, and racial representation were created. Black, Hispanic, and female patients were significantly underrepresented in the RCTs for lymphoma when compared to the disease burden, 3.6% [95% CI: 2.8, 5.4] vs. 14.6% [95% CI: 13.8, 15.3], 6.7% [95% CI: 5.5, 7.9] vs. 16.3% [95% CI: 15.5, 17.1], and 39.1% [95% CI: 37.3, 40.9] vs. 42.7% [95% CI: 42.3, 43.1], respectively. White and male patients were overrepresented. More counties with higher mortality rates and racial minority representation had low access to the trials, particularly for cHL in the Southern region of the US. There are significant racial misrepresentation in pivotal RCTs in the US, and geographic distribution of these trials may not provide easy access to all patients in need. Disparities in enrollment should be corrected to make results applicable to all populations.
Keyphrases
- end stage renal disease
- clinical trial
- newly diagnosed
- chronic kidney disease
- ejection fraction
- hodgkin lymphoma
- randomized controlled trial
- healthcare
- drug administration
- african american
- metabolic syndrome
- risk factors
- coronary artery disease
- patient reported outcomes
- big data
- deep learning
- case report
- human health
- glycemic control
- double blind