Abstract Number:
1435
Submission Type:
Invited Paper Session
Participants:
Satrajit Roychoudhury (1), Kannan Natarajan (2), Robert A. Tumasian III (3), Demissie Alemayehu (1), Jakub Hlávka (4), Laura Fernandes (5)
Institutions:
(1) Pfizer, N/A, (2) Pfizer Inc., NY, (3) U.S. Food & Drug Administration (FDA), N/A, (4) University of Southern California, N/A, (5) COTA Healthcare Inc., N/A
Chair:
Discussant(s):
Session Organizer:
Speaker(s):
Session Description:
Lack of diversity in clinical trials has long been an issue, driven by challenges with recruitment and participation. For example, women and people from most racial and ethnic groups in the United States have historically been under-represented in clinical trials. Inadequate representation of some population groups may lead to an incomplete understanding of the safety and efficacy of medical products, and seriously restrict the generalizability of trial findings. As a result, new medical products may not be beneficial to all people who need them, and existing inequities in outcomes among various population groups may remain unchanged or worsen. Although much work has focused on study-level strategies, organizations must make systemic changes to how clinical trial implementation to achieve sustainable support for diversity and inclusion in clinical trials. US Food and Drug administration also identifies the problem and issued a draft guidance for public opinion in 2023.
New innovations in statistics and data science to support clinical trial design plays an important role here. Insights from trial external data, collected historically or current time can play an important role to enhance the representation of the population and better interpretation of treatment effect. Identifying variations at the clinical trial stage of drug development allows the best treatment strategies to be designed for all groups with a particular condition, saving money and improving patient experiences. For example, use of synthetic control model for everyone can help reducing the diversity gap in clinical trial. On the hand, use of appropriate statistical and machine learning models helps to extract appropriate treatment information from heterogeneous groups. By properly collecting, analyzing, and applying data on patient demographics, truly representative clinical trials can be designed. This will have benefits across the scope of clinical research, with trials becoming more efficient, cost-effective, and equitable. In addition, diversity in trial groups is key to restoring trust in the clinical trial process and, most importantly, improving health outcomes for everyone.
The session will have two eminent speakers and two discussants from academia, industry and FDA who are well experienced about the issue, possible remedies, and current regulatory landscape. The First speaker Dr. Jakub Hlávka (University of South California) will present "The Clinical and Economic Toll of Insufficient Diversity in Clinical Trials: Do We Miss our Best Shots?," which illustrates the overarching goal of the U.S. investment in biomedical research is to use of data-driven approaches to improve the health and well-being of the entire U.S. population. He'll also discuss the identified seven potential threats to this goal posed by lack of representation in clinical research. The second speaker, Dr Laura Fernandes (COTA Healthcare) will present "The Roadmap for Making Clinical Trials More Diverse and Representative". Her presentation will cover the lack of diversity in clinical trials and the different efforts taken by the regulatory agencies to remedy them. Efforts by the industry to improve diversity using data-driven approach and the tools for alternative clinical trial designs will be presented. Finally, two discussants Dr. Kannan Natarajan (Pfizer Inc.) and Dr. Robert A Tumasian III (US FDA) will provide additional reflections f industry and regulatory perspectives.
Sponsors:
Biopharmaceutical Section 1
Committee on Minorities in Statistics 2
Health Policy Statistics Section 3
Theme:
Statistics and Data Science: Informing Policy and Countering Misinformation
Yes
Applied
Yes
Estimated Audience Size
Large (150-275)
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