Grand Rounds July 18, 2025: State of Clinical Trials: An Analysis of ClinicalTrials.gov (Adrian F. Hernandez, MD, MHS; Rebecca D. Sullenger, MPH; Sara Bristol Calvert, PharmD; Karen Chiswell, PhD; Christopher J. Lindsell, PhD)

Speakers:

Adrian F. Hernandez, MD, MHS
Executive Director
Duke Clinical Research Institute

Rebecca D. Sullenger, MPH
Duke University School of Medicine
MD Student | Class of 2026

Panelists:

Sara Bristol Calvert, PharmD
Director of Projects
Clinical Trials Transformation Initiative

Karen Chiswell, PhD
Statistical Scientist
Duke Clinical Research Institute

Christopher J. Lindsell, PhD
Director, Data Science and Biostatistics
Duke Clinical Research Institute

Keywords

Clinical Trials; Enrollment; Pragmatic Clinical Trials; Policy; Data Science

Key Points

  • A study of clinical trials from 2007 to 2010 found that the field was dominated by small trials and contained significant heterogeneity in methodological approaches, including reported use of randomization, blinding, and Data Monitoring Committees.
  • Clinical trials in the United States may be limited by legal, regulatory, and cost-related barriers. In a study of patient enrollment for cardiovascular clinical trials, the authors concluded that the U.S. had more trial sites than Eastern Europe or South America, but enrolled significantly fewer patients per site. These trends highlight the need for improved clinical trial infrastructure.
  • The presenters noted several promising trends in the field: growth in pragmatic clinical trials; high interest in clinical trial innovation from regulatory bodies and funding agencies; and the rapidly evolving capacity of clinical trials, particularly around accessibility.
  • The presenters provided an updated picture of the clinical trials landscape in the U.S., based on retrospective analyses of interventional clinical trials registered on ClinicalTrials.gov between 2018 and 2024.
  • They found that many trials remain small, lack a control group, and are incomplete after 5 years. Although small clinical trials without controls may be appropriate or necessary in specific contexts, such trials are also less likely to produce actionable data.
  • National policies prioritizing a more rapid, rigorous evidence generation system will likely be necessary to create a clinical trial ecosystem best equipped to advance public health.
  • In light of these insights, the team shared 5 potential policy approaches to improve the evidence-generation system in the U.S.:
    • Streamline trial start-up processes, institutional review board approvals, and contracting;
    • Enable scalable technologies to support greater participation;
    • Invest in modern clinical trial design strategies;
    • Require public reporting of key performance indicators and pay-for-performance results; and
    • Create stronger data sharing requirements and accountability rules.

Discussion Themes

Though the team utilized existing fields in ClinicalTrials.gov for their data, future research may utilize the key word search (i.e. adaptive platform trials) or natural language processing to investigate the state of clinical trials.

The value of small (<100 participants) trials was debated by the panelists. Though they do have a time and place, the high proportion of Phase III trials that enrolled less than 100 participants was surprising and concerning.

There are some limitations to ClinicalTrials.gov, namely in data entry. The more complex the trial, the more difficult it is to submit in a timely fashion. The system may post a barrier to embracing modern clinical trial design strategies.

Academia will also need to make policy changes to facilitate a healthier clinical trials ecosystem. The way career development and promotion pathways are structured, researchers are incentivized to lead small, potentially duplicative trials. Institutions need to reward, compensate, and value individual contributions to large-scale programs; i.e., the informative trial over the individually led trial.

June 7, 2019: In Dreams Begin Responsibilities: Data Science as a Service—Using AI to Risk Stratify a Medicare Population and Build a Culture (Erich Huang, MD, PhD)

Speaker

Erich S. Huang, MD, PhD
Co-Director, Duke Forge
Departments of Biostatistics & Bioinformatics and Surgery
Duke University School of Medicine

Topic

In Dreams Begin Responsibilities: Data Science as a Service—Using AI to Risk Stratify a Medicare Population and Build a Culture

Keywords

Data science; Data liquidity; Data standards; Machine learning; Duke Forge; Application programming interface; Artificial intelligence

Key Points

  • Duke Forge focuses on bringing the best methodological approaches to actionable data problems in health. It is motivated by a framework of value-based healthcare to address societal inequities in health.
  • Essential components to building a data science culture include clinical subject matter expertise, quantitative and methodological expertise, and software architecture and engineering expertise, along with interoperable tools and applications.
  • Like freight shipping containers, health-relevant data needs standardized containers that make any type of data easy to pack, grab, combine, and move around. The aim should be to build a “data liquidity ecosystem” equivalent to freighters, cranes, trains, and trucks that facilitate the logistics of health data transport.

Discussion Themes

If we’re trying to build an ecosystem, then the electronic health record (EHR) platform needs to be evaluated by whether it is truly participatory in this ecosystem. If not, then its deficiencies must be remediated.

The faster we can move to the cloud and use building blocks that “snap” together, the faster we can get answers. We want to be building applications instead of infrastructure.

Algorithms don’t have ethics; some have hidden biases. Algorithms need to be scrutinized and tested for such biases. They also must be secured so they cannot be manipulated.

Read more about Duke Forge and check out articles on the blog.

Tags

#pctGR, @Collaboratory1, @DukeForge