Less than 10% of rare diseases have approved treatments, and the lack of reliable end points to measure the clinical benefit of treatments exacerbates the issue. The use of clinical outcome assessments (COAs) has increased over time, and COA use has become a common tool for establishing the clinical benefit of novel therapies.
Even when well-established predictive or prognostic biomarkers are available, COAs provide valuable information about how patients experience treatment, and sensitivity to clinical change is becoming the top priority in COA selection, especially in central nervous system (CNS) and rare-disease trials.
Sensitivity Matters
COAs are essential for evaluating whether clinical trial data will withstand regulatory review and real-world validation, but the increasing complexity of clinical trials makes it more challenging to choose the right assessment.
Statistical significance alone is no longer sufficient to demonstrate patient benefits. Moreover, small trials, such as those common in CNS and rare-disease research, might mean that statistical significance isn’t feasible.
“The shift [toward prioritizing sensitivity to meaningful clinical change] is being driven by the realities of modern CNS and rare-disease development, where small sample sizes, heterogeneous populations, and slow disease progression can make conventional significance thresholds difficult to achieve,” explains J. Lynsey Psimas, Ph.D., licensed clinical psychologist and director of business development at Pearson Assessments. “Sponsors increasingly need end points that can detect subtle but clinically relevant changes that reflect real patient benefit.”
In fact, more than half of researchers prioritized COAs sensitive to meaningful clinical change, according to a new Pearson report, “Unlocking the Power of COAs in Clinical Research.” The report also found that 80% of researchers felt confident in their ability to access COAs sensitive enough to detect meaningful clinical change in CNS or rare-disease populations, while 20% said they lacked confidence.
Improving COA Selection
As the emphasis shifts toward meaningful change, selecting the right COA becomes not just a methodological decision but also a strategic one.
COAs should be selected to fit the target population, capture the right concepts, align with how the investigational product is expected to work, and match the abilities of the trial participants. Too often, clinical researchers select instruments based on familiarity rather than fit-for-purpose evidence, fail to assess sensitivity to change in the target population, or neglect to align the COA to the mechanism of action. These gaps can significantly affect clinical trials.
“The greatest risk is a false negative trial, where the therapy may work but the end point fails to detect the benefit,” Psimas says. “This can lead to failed studies, wasted investment, and delayed patient access.”
But choosing the right COA can be challenging. Instruments must capture the right concepts of interest, be sensitive enough across disease-severity ranges, and maintain administration consistency across sites and raters while minimizing patient burden.
The Role of AI
Artificial intelligence (AI) is playing a growing role in COAs. Digital COAs have improved accessibility and global access in decentralized trials, and compared with clinician observations, fixed questionnaires, and other manual tools, AI is less time- and resource-intensive and can analyze large datasets to assess real-world patient experiences.
While AI can improve the speed, efficiency, and accuracy of eCOA instruments, streamlining processes, accelerating clinical trial timelines, and improving the quality of collected data, Psimas notes, “AI is not replacing established COAs. But it is expanding what is possible through smarter eCOA workflows and deeper interpretation of longitudinal patient-experience data.”
The ability to analyze patterns in patient behavior, demographics, and assessment completion can identify patients at risk of dropping out or missing assessments and suggest personalized engagement strategies.
In CNS and rare-disease trials, prioritizing sensitivity to meaningful clinical change allows researchers to better capture how patients feel, function, and experience treatment. This shift can mean the difference between detecting improvements in cognition, reduced caregiver burden, better communication, or delayed disease progression, and having those meaningful changes go unreported.
Clinical researchers understand the downsides. Pearson research found that 94% of researchers wanted to learn more about selecting appropriate end points for CNS or rare-disease trials, and 91% wanted more information about best practices for COA selection.
Pearson partners with clinical researchers to streamline the process of selecting validated COAs, such as Vineland-3, RBANS, D-KEFS, and Bayley-4, that are sensitive to clinical change and can detect meaningful outcomes, helping to power CNS and rare-disease trials.
For deeper insights into today’s most common COA challenges and practical ways to address them, read the full report, “Unlocking the Power of COAs in Clinical Research.”
