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Logistical Challenges in Platform Randomized Trials - Operational Realities That Curb the Promise of Efficiency
Kristian Thorlund1,2, Jay J H Park1,3, Matt P Wise4
1Department Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Abstract:
Platform randomized trials offer a flexible framework for evaluating multiple therapies within a shared long-term infrastructure, yet their implementation poses substantial logistical and operational challenges. This paper examines three interrelated features that frequently determine the feasibility and efficiency of platform trials: 1) infrastructure and setup costs; 2) training, site management, and partial center participation; and 3) adapting to changing clinical practice and diagnostic criteria. While platform designs are often promoted for their efficiency, the establishment and maintenance of long-term infrastructure require greater upfront investment, more complex governance structures, and ongoing retraining compared to conventional randomized trials. Site heterogeneity and partial participation can create inconsistencies in implementation, data quality, and recruitment momentum. Over the long term, evolving standards of care and diagnostic definitions further complicate cross-arm comparability and risk introducing time-confounding. Rather than assuming platform trials are inherently more efficient, we argue that their success depends on sustained resourcing, harmonization efforts, sustained dedication from trial sites, and a robust training infrastructure. To address these challenges, we describe a trial-to-platform approach: a phased strategy in which a conventional multicenter trial is launched with modular, expandable systems that can later evolve to a platform trial. This approach allows for early learning, milestone-based funding, harmonization before full expansion, thereby reducing cost inefficiency and operational drift. Practical recommendations are provided for funders and trialists on how to structure, resource, and govern platforms to ensure scalability, reproducibility, and long-term sustainability.
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