Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets

• URL: http://arxiv.org/abs/2408.01375v1
• Date: Fri, 2 Aug 2024 16:32:30 GMT
• Title: Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets
• Authors: Victor Borza, Andrew Estornell, Ellen Wright Clayton, Chien-Ju Ho, Russell Rothman, Yevgeniy Vorobeychik, Bradley Malin,
• Abstract summary: We introduce a computational approach to adaptively allocate recruitment resources among sites to improve representativeness. In simulated recruitment of 10,000-participant cohorts from medical centers, we show that our approach yields a more representative cohort than existing baselines.
• Score: 23.462552062769426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large participatory biomedical studies, studies that recruit individuals to join a dataset, are gaining popularity and investment, especially for analysis by modern AI methods. Because they purposively recruit participants, these studies are uniquely able to address a lack of historical representation, an issue that has affected many biomedical datasets. In this work, we define representativeness as the similarity to a target population distribution of a set of attributes and our goal is to mirror the U.S. population across distributions of age, gender, race, and ethnicity. Many participatory studies recruit at several institutions, so we introduce a computational approach to adaptively allocate recruitment resources among sites to improve representativeness. In simulated recruitment of 10,000-participant cohorts from medical centers in the STAR Clinical Research Network, we show that our approach yields a more representative cohort than existing baselines. Thus, we highlight the value of computational modeling in guiding recruitment efforts.

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