Can We Predict Trial Success? From ‘Feasibility’ to Predictive ‘Readiness’
What learning science has taught us about the drivers and predictors of change—and applying those to clinical research practice.
So much has been written about site feasibility over the past decade—even a cursory review of Applied Clinical Trials magazine, for instance, will identify ~20 articles, press releases, and interviews describing site feasibility services, solutions, toolkits, and best practices. And this is just a small snapshot of the “research” and promotion of site feasibility that overwhelms our community. With all that has been written and presented, it seems logical to ask if these site feasibility efforts have provided meaningful benefits.
Perhaps not surprisingly, current site and trial performance data provide a striking answer:
- 70% of trials experience start-up delays
- 80% of trials fail to meet on-time enrollment
- 45% of trials miss original projected timelines
If the goal of site feasibility is to “predict” if a site will be successful in conducting a study and the performance data suggests that sites continue to struggle, then maybe it’s time to rethink our principle approach to predicting performance. To be clear, we need to continue to refine and enhance the predictive validity of site feasibility, but there are other evidence-based predictive measures of change that should be immediately used by clinical research professionals to minimize start-up delays, accelerate enrollment, and optimize trial performance.
In each of my prior
From learning to doing: Evidence-based predictors of performance
To summarize merely 50 years of evidence: learning science has demonstrated six characteristics of a learner in a training experience that are highly predictive of application of learning (i.e., behavior change). The more these characteristics are surfaced during a training experience, the more likely performance will improve. In other words, we know definitively that how and what a learner thinks while learning is actually our most accurate predictor of change. So what are these predictive characteristics?
1. Confidence (Self-efficacy)
Learner confidence, or self-efficacy, reflects the belief in one’s ability to execute specific tasks or behaviors.
2. Reflection
Reflection involves the process of evaluating experiences and recognizing areas for improvement.
3. Curiosity
Curiosity drives individuals to explore, seek out new information, and remain engaged.
4. Grit (resilience)
Duckworth’s research on grit—
5. Intention to change (commitment to change)
Ajzen demonstrated that
6. Self-regulation
Self-regulation, the capacity to monitor and manage one’s learning process, plays a critical role in behavior change.
Importantly, these are characteristics of how and what a person thinks as they learn. In
Moving to feasibility plus predictive readiness
Site feasibility, as an “S-Frame” intervention, has a critical place in planning and conducting clinical trials, but it is simply not a strong predictor of trial success. The success of a study is more than having adequate logistics, resources, or experience—that’s not how performance works. To maximize our ability to predict trial success, we must consider the actual predictive drivers of behavior change. By focusing on these six training-based predictors, we can design training programs that not only convey knowledge but also foster lasting change. Ultimately, the purpose of trial start-up and site training is to empower professionals to act, transforming insights into better trial enrollment and execution, and accelerating advancements in patient care.
Brian S. McGowan, PhD, FACEHP, is Chief Learning Officer and Co-Founder, ArcheMedX, Inc.
Articles in this issue
Newsletter
Stay current in clinical research with Applied Clinical Trials, providing expert insights, regulatory updates, and practical strategies for successful clinical trial design and execution.
Related Articles
- Everything to Know About FDA’s Push Towards Radical Transparency in 2025
September 17th 2025
- IQVIA and Veeva Join Forces to Improve Efficiency and Patient Outcomes
September 17th 2025
- Managing Background Therapies in the NIMBLE Phase III Trial
September 17th 2025
- Generative AI Transforms Clinical Study Report Development
September 16th 2025