FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection

• URL: http://arxiv.org/abs/2305.19407v1
• Date: Tue, 30 May 2023 20:44:14 GMT
• Title: FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection
• Authors: Brandon Theodorou, Lucas Glass, Cao Xiao, and Jimeng Sun
• Abstract summary: This paper focuses on the trial site selection task and proposes FRAMM, a deep reinforcement learning framework for fair trial site selection. We focus on addressing two real-world challenges that affect fair trial sites selection: the data modalities are often not complete for many potential trial sites, and the site selection needs to simultaneously optimize for both enrollment and diversity. We evaluate FRAMM using 4,392 real-world clinical trials ranging from 2016 to 2021 and show that FRAMM outperforms the leading baseline in enrollment-only settings.
• Score: 55.2629939137135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite many efforts to address the disparities, the underrepresentation of gender, racial, and ethnic minorities in clinical trials remains a problem and undermines the efficacy of treatments on minorities. This paper focuses on the trial site selection task and proposes FRAMM, a deep reinforcement learning framework for fair trial site selection. We focus on addressing two real-world challenges that affect fair trial sites selection: the data modalities are often not complete for many potential trial sites, and the site selection needs to simultaneously optimize for both enrollment and diversity since the problem is necessarily a trade-off between the two with the only possible way to increase diversity post-selection being through limiting enrollment via caps. To address the missing data challenge, FRAMM has a modality encoder with a masked cross-attention mechanism for handling missing data, bypassing data imputation and the need for complete data in training. To handle the need for making efficient trade-offs, FRAMM uses deep reinforcement learning with a specifically designed reward function that simultaneously optimizes for both enrollment and fairness. We evaluate FRAMM using 4,392 real-world clinical trials ranging from 2016 to 2021 and show that FRAMM outperforms the leading baseline in enrollment-only settings while also achieving large gains in diversity. Specifically, it is able to produce a 9% improvement in diversity with similar enrollment levels over the leading baselines. That improved diversity is further manifested in achieving up to a 14% increase in Hispanic enrollment, 27% increase in Black enrollment, and 60% increase in Asian enrollment compared to selecting sites with an enrollment-only model.

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