Demographic clinical trial diversity assessment methods: Use of real-world data
Diversity in clinical trials is defined by the inclusion of clinical trial participants from various demographic groups that are representative of the broader population impacted by a disease state. Diversity in clinical trials is critical in identifying potential differences in safety and efficacy...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
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| Series: | Contemporary Clinical Trials Communications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2451865425000067 |
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| Summary: | Diversity in clinical trials is defined by the inclusion of clinical trial participants from various demographic groups that are representative of the broader population impacted by a disease state. Diversity in clinical trials is critical in identifying potential differences in safety and efficacy of treatments across races, ethnicities, ages, sexes, or other variables. In the United States, clinical trial diversity is often benchmarked against US Census data, which may limit the representativeness of patient demographics in clinical trials. Disease-specific, demographic estimates from real-world data (RWD) can facilitate benchmarking of clinical trials, support trial enrollment and the development of trial diversity plans. Notably, development and dissemination of these estimates from RWD can be challenging without a standardized process. To address this issue, we developed a new evaluation framework to assess patient demographics and characteristics within specific disease populations using RWD and disease population estimates.Suitable databases were identified using predefined criteria such as accessibility to patient-level data, availability of all demographic variables of interest, sufficient sample size of the disease population, and availability of population weights to enhance generalizability. Concurrent data were gathered via targeted literature reviews for each disease condition. Together, this data was used to create disease-specific, demographic estimate profiles to inform diverse enrollment goals for prospective clinical trials. We present two examples of application of this framework to illustrate the results in the case of two disease states, rheumatoid arthritis and stroke. |
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| ISSN: | 2451-8654 |