Data visualization explorer: A tool for participant representation in pivotal trials of FDA-approved medicinal products

Ariel B Carmeli; Laura Meloney; Barbara E Bierer

doi:10.1016/j.patter.2023.100713

Data visualization explorer: A tool for participant representation in pivotal trials of FDA-approved medicinal products

Patterns (N Y). 2023 Mar 29;4(5):100713. doi: 10.1016/j.patter.2023.100713. eCollection 2023 May 12.

Authors

Ariel B Carmeli^{1

2}, Laura Meloney³, Barbara E Bierer^{3

4}

Affiliations

¹ Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115 USA.
² Harvard Business School, Boston, MA 02115, USA.
³ Multi-Regional Clinical Trials Center of Brigham and Women's Hospital and Harvard, Division of Global Health Equity, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
⁴ Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.

Abstract

This paper presents a 2015-2021 US Food and Drug Administration (FDA) Drug Trials Snapshots (DTS) Data Visualization Explorer-an interactive data visualization web-based tool (https://arielcarmeli.shinyapps.io/fda-drug-trial-snapshots-data-explorer) built in R and based on publicly available FDA clinical trial participation data and disease incidence data from the National Cancer Institute and Centers for Disease Control and Prevention. FDA data can be explored by race, ethnicity, sex, age group, therapeutic area, pharmaceutical sponsor, and approval year for clinical trials that supported each of the 339 FDA drug and biologic approvals between 2015 and 2021. This work provides several advantages relative to past literature and DTS reports: a dynamic data visualization tool; race, ethnicity, sex, and age group data presented in one place; data on sponsor; and emphasis on data distributions instead of averages. We present recommendations for improved data access, reporting, and communication to assist leaders in making evidence-based decisions to improve trial representation to enhance health equity.

Keywords: clinical trials; data science; data visualization; demographic representation; health equity; open data; transparency.