Related Experiment Video
Updated: Apr 12, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
The power, potential of real-world data in randomized controlled trials: proceedings from a multistakeholder think
Nina Nouhravesh1,2, Jennifer G Jackman1, Adrian F Hernandez1
1Duke Clinical Research Institute, Duke University School of Medicine, Durham Center, DUMC #3850, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA.
Abstract:
Randomized controlled trials (RCTs) remain the gold standard for evaluating medical interventions, but they often face challenges related to patient recruitment, cost, and efficiency. Real-world data (RWD) has emerged as a valuable tool to enhance trial design, improve patient identification, and support regulatory decision-making. However, integrating RWD into RCTs presents methodological, regulatory, and operational challenges. To address these issues, a think tank was convened in May 2024 at the Duke Clinical Research Institute, bringing together experts from academia, industry, healthcare systems, regulatory agencies, and patient advocacy groups. Discussions focused on three key areas: optimizing patient identification and outcome assessment, leveraging RWD for safety assessments, and using RWD in RCTs supporting regulatory approval. RWD has the potential to simplify eligibility criteria, enhance recruitment through artificial intelligence, and provide practical endpoints for evaluating treatment effects. The think tank underscored the need for collaboration across stakeholders to address challenges, such as data inconsistencies, privacy concerns, and infrastructure limitations. The event concluded with actionable recommendations, including the following: (1) standardizing RWD sources to ensure consistency and improve interoperability across healthcare systems, (2) developing regulatory frameworks that define acceptable use cases for RWD in clinical trials, (3) enhancing data quality through robust validation methodologies and real-time monitoring, (4) investing in artificial intelligence-driven patient identification tools to streamline recruitment, and (5) fostering multi-stakeholder collaboration to align expectations and share best practices. Moving forward, implementing these strategies will be critical to fully harness the potential of RWD in clinical research and improve trial efficiency.
More Related Videos
04:53A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
Published on: September 20, 2019
09:33Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
Published on: December 23, 2022
Related Concept Videos
Randomized Experiments
Simple randomization
Simple...
Blinding
Data Collection by Experiments
An example of the experimental method is a public...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Clinical Trials
There are four phases in a clinical trial. A phase one...
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs