Speakers
Christopher J. Lindsell, PhD
Vice Dean for Data Science and AI
Duke University School of Medicine
Lloyd Michener, MD
Professor & Chair Emeritus
Department of Family Medicine & Community Health
Duke University School of Medicine
Adjunct Professor, Public Health Leadership
UNC Gillings School of Global Public Health
Justin B. Moore, PhD, MS
Professor and Chair (interim)
Department of Implementation Science
Director for Dissemination, Implementation, and Continuous Quality Improvement
Clinical and Translational Science Institute
Wake Forest School of Medicine
Boris Volkov, PhD
Evaluation Director and Assistant Professor
University of Minnesota Clinical and Translational Science Institute & Institute for Health Informatics
Reuben Adatorwovor, PhD
Assistant Professor of Biostatistics
Institute of Informatics, Data Science and Biostatistics
Department of Emergency Medicine
Washington University in St. Louis
Keywords
Study Design; Translation; Translational Science; Failure; Engagement; Biostatistics; Informatics; Implementation; Evaluation
Key Points
- Translational failure often begins before the trial. The labels we use – eg, recruitment failure, stakeholder resistance, and poor generalizability – indicate where failure becomes visible, not where it originates. However, trial design remains largely unevidenced. If a decision determines whether downstream work can succeed, the presenters argued, the basis for that decision belongs in the scientific record.
- A translational design study is a systematic inquiry conducted before, or alongside, planned translational work. It seeks to reduce a named design uncertainty. The category is defined by intent, not by method; qualitative, quantitative, and mixed methods all qualify. The presenters introduced 4 lenses on design uncertainty: community engagement; evaluation; biostatistics and informatics; and implementation science. These are to be used in conjunction, not held as separate disciplines.
- Attendees were encouraged to submit their design-phase work for publication, especially if their findings forced them to change course. If it includes a clear design question, an explicit assumption, reproducible methods, and results that others can apply, adapt, and build on, it qualifies.
Discussion Themes
While industry focuses on scalability, the presenters argued that the academic community should lead in design innovation.
ACTIV-6 is a real-world example. In that large decentralized trial, evidence-based protocol changes served as a model for transferable design learning.