P217: EFFECT: An Epidemiological Framework For Equitable Representation of Racial and Ethnic Populations in Clinical Trials
Poster Presenter
Andrew R Marley
Senior Epidemiologist
Parexel Spain
Objectives
To establish a framework to inform equitable recruitment of underrepresented racial and ethnic populations into sponsored US clinical trials (CTs) in response to the April 2022 draft guidance from the US Food and Drug Administration (FDA).
Method
Indirect standardization methods based on 2020 US Census Bureau demographic proportions and disease epidemiology were used to create a framework for the calculation of equitable recruitment goals by race and ethnicity. The framework was applied to various indications from diverse treatment areas.
Results
The EFFECT framework is a two-phase, five-step framework solely intended to provide step-by-step guidance for the determination of enrollment goals based on Census proportions and disease epidemiology as a first step towards more equitable biomedical research. Phase 1, the transition from reference population to standardized population, includes the following steps: Step 1 – calculation of individual standardized incidence per racial/ethnic group using disease epidemiology data (real-world data and/or medical literature); Step 2 – calculation of overall standardized incidence across all racial/ethnic groups; and Step 3 – use of standardized incidence from step 2 to calculate proportions for each race/ethnicity. Phase 2, the transition from a standardized population to the trial target population includes two subsequent steps to support equitable representation: Step 4 –using census proportions to set enrollment goals in the absence of specific epidemiology data for racial/ethnic groups historically underrepresented in medical literature; and Step 5 –using census proportions as enrollment goals when standardized proportions among historically underrepresented racial/ethnic groups do not reflect Census representation. This framework consistently provided recruitment goals reflective of US demography and disease epidemiology for 5 diverse indications (COVID-19, multiple sclerosis, metastatic colorectal cancer, primary discoid lupus erythematosus, and multiple myeloma). This framework informs representative recruitment goals among indications with considerable variation in racial/ethnic incidence, further indicating methodological robustness. Deviations from this framework may be necessary where differences in disease epidemiology can be explicitly attributed to underlying biological, physiological, or genetic phenomena, and not to differences in treatment access/barriers, behavior/lifestyle, migration patterns, or structural inequity.
Conclusion
Minority populations have long been underrepresented in biomedical research, contributing to health inequities experienced by many within the US population. Adequate representation and inclusion of racial and ethnic minorities in CTs is critical for generalizability of clinical findings, ensuring access to effective therapeutics, promoting trust, reducing health disparities, and evaluating potentially differing effects by race and ethnicity. The recent FDA guidance has emphasized the importance of equitable enrollment into sponsored clinical trials. Our EFFECT framework leverages these guidelines to provide a step-by-step methodology that promotes the creation of equitable enrollment goals based on US Census data and disease epidemiology. This framework is intended to be implemented with an understanding of the feasibility of enrolling patients in the US for the target indication, to ensure that the goals are achievable within the current landscape. This framework is intended solely to address equitable patient enrollment, and not meant to address issues of retention, loss to follow up, and statistical power for subgroup analyses, all of which are also important for minority inclusion in biomedical research. Sponsors are encouraged to incorporate the EFFECT framework into trial planning, and documentation. Further research into the EFFECT framework include testing it in larger samples, applying it to additional indications to further validate its robustness, and creating/examining feasibility strategies to help achieve enrollment goals . Improvement and expanded application into geographical locations outside of the US, rare diseases where enrollment targets are low, and to other minority groups, such as sexual and gender minorities, is invited.
