WITHDRAWN Leveraging Real World Data Sources to Estimate Ranges for Distributions of Patient Ethnicity and Race in U.S. Clinical Trials

OBJECTIVES: To demonstrate a process that evaluates multiple RWD sources from which diversity targets for clinical trials can be generated to help improve representation of US subpopulations, specifically race and ethnicity (R/E), in specific disease areas.

METHODS: Query patient counts, split by race & ethnicity, for each ICD10 or sets of ICD10s in a variety of data sources: Longitudinal patient electronic medical records systems (EMRs); Integrated Health Network EMR; Ambulatory EMR; Government sources of publicly available electronic datasets including CDC’s cross-sectional household survey (National Health Interview Survey); Published literature and historical clinical trials in the indication of interest.

RESULTS: For most diseases and health conditions, no single data source provides definitive distributions of patients by race and ethnicity. Rather, each data source must be evaluated by geographic representativeness; socio-economic representativeness; size of raw/unweighted population (w and w/o condition); size of raw/unweighted population (with condition only); sensitivity to indication/condition; specificity of indication/condition; granularity of racial categories; and appropriate balancing of estimates applied.

CONCLUSIONS: Real World Data can be leveraged to support or develop clinical trial diversity goal ranges. Decision-making relies on both quantitative and qualitative considerations to evaluate potential R/E diversity. Estimates in the scientific literature are also important to establish background and contextualization of R/E estimates through a synthesis of multiple studies or cohorts. Future investigations will demonstrate several indication-specific examples evaluating diversity within each data source so that distributions can inform clinical trial planning. Data informed and evaluated diversity goals can help enable more robust trial enrollment planning and strategies that can then deliver more inclusive studies across racial and ethnic groups.