A novel approach to assessing disparity in representativeness of clinical trial participants within a large midwestern healthcare system

Abstract

Background: Representativeness in clinical trials (CT) serves as a metric of access to healthcare and reflects differences that may determine differential efficacy of medical interventions; thus, quantifying representativeness in CT participation is critical. Methods: This retrospective, descriptive study utilized patient demographic data extracted from the largest Midwestern non-profit healthcare system. Using data between January 1, 2019 and December 31, 2021, a CT Participant Sample of 4,537 system patients who were active CT participants was compared to a CT Patient Population of 195,726 system patients receiving care by the PI of active CTs, which represented the target population. Chi-square goodness-of-fit tests were used to test differences in distributions of demographic variables between groups, indicating disparity in CT participation. Two metrics adapted from literature - participation incidence disparity (PID) and participation incidence ratio (PIR) - were calculated to quantify absolute and relative disparity in representativeness proportions, respectively. Descriptive approaches to assessing representativeness are also provided. Results: Results showed significant differences by race/ethnicity (χ2 = 50.64; p < 0.0001), age categories (χ2 = 56.64; p < 0.0001), and insurance (χ2 = 41.29; p < 0.0001). PID and PIR metrics revealed reduced CT participation among non-White racial/ethnic groups and increased CT participation among White Non-Hispanic patients. Further, CT participants ≥80 or Worker's Compensation were underrepresented while those with Self-Pay insurance were overrepresented as CT participants. Conclusions: Despite progress, continued efforts to not only enroll participants into CTs that are representative of the healthcare system and region, but also to better assess representativeness quantitatively are still needed.

Keywords

• Clinical trial
• Disparity
• Representativeness
• Clinical trial participation