R-index: a standardized representativeness metric for benchmarking diversity, equity, and inclusion in biopharmaceutical clinical trial development.

Background: Diversity, equity, and inclusion pertaining to race, ethnicity, and related concepts have historically been underrepresented in clinical trials for pharmaceutical drug development, although this is an increasing topic for regulators, payers, and patient advocacy groups. We aimed to develop a summary statistical measure to assess such representativeness.

Methods: A statistical measure using population demographic parameters derived from performance metrics through verbal autopsy research was proposed for using population frameworks in the UK. The summary measure, R-index, was demonstrated using simulation data with population frameworks from the UK (116 Roche UK clinical trials 2013-2022) and then using published clinical trial results (NCT02366143 [March 1, 2015-September 15, 2017], NCT04368728 [July 27, 2020-October 9, 2020], and NCT04470427 [July 27, 2020-November 25, 2020]). R-index was further proposed for use with benchmarking performance in representative trial development for internal processes, external benchmarking, and performance tracking in clinical trial development.

Findings: R-index was derived from a standardized statistical measure called the L1 norm, or Manhattan distance, and then normalized to the maximum theoretical error observed in some populations using population framework or ontology for reporting concepts such as race, ethnicity, and other dimensions of diversity used to characterize patient cohorts. R-index demonstrated desirable qualities in demonstration simulations, including a range of 0-1, ease of calculation and use, and interpretability and flexibility, as data standards in the space of inclusive research continue to develop.

Interpretation: R-index is an interpretable, accessible summary statistic that may be useful for tracking and benchmarking representativeness in inclusive research and related domains. R-index is adaptable to different population frameworks and ontologies across different settings and considerations in terms of underlying population variables.

Funding: F. Hoffmann-La Roche Ltd/Genentech, Inc.