Clinical trial site matching with improved diversity using fair policy learning

TL;DR

This paper introduces a fair policy learning approach for clinical trial site matching that balances feasibility and diversity, ensuring inclusion of multiple patient groups while maintaining trial efficiency.

Contribution

It formulates trial site selection as a ranking problem with multi-group fairness constraints and proposes a novel demographic parity-based criterion for diverse patient access.

Findings

Achieves diverse patient access in trial site selection

Maintains high enrollment numbers

Outperforms baseline methods in real-world tests

Abstract

The ongoing pandemic has highlighted the importance of reliable and efficient clinical trials in healthcare. Trial sites, where the trials are conducted, are chosen mainly based on feasibility in terms of medical expertise and access to a large group of patients. More recently, the issue of diversity and inclusion in clinical trials is gaining importance. Different patient groups may experience the effects of a medical drug/ treatment differently and hence need to be included in the clinical trials. These groups could be based on ethnicity, co-morbidities, age, or economic factors. Thus, designing a method for trial site selection that accounts for both feasibility and diversity is a crucial and urgent goal. In this paper, we formulate this problem as a ranking problem with fairness constraints. Using principles of fairness in machine learning, we learn a model that maps a clinical trial description to a ranked list of potential trial sites. Unlike existing fairness frameworks, the group membership of each trial site is non-binary: each trial site may have access to patients from multiple groups. We propose fairness criteria based on demographic parity to address such a multi-group membership scenario. We test our method on 480 real-world clinical trials and show that our model results in a list of potential trial sites that provides access to a diverse set of patients while also ensuing a high number of enrolled patients.