Selecting the proper clinical outcome assessment (COA) can set the tone for an entire trial’s success. Yet for many clinical researchers, COA selection remains one of the most misunderstood and delayed aspects of study design. 

The result? Missed alignment, slower timelines, and data that may not fully capture what truly matters.

In a time when COAs are more nuanced, critical, and complex than ever, researchers can turn to best practices to move forward efficiently while maintaining scientific and regulatory rigor.

Why COA selection is more critical and complex than ever

The role of COAs has expanded dramatically in modern trials. No longer just a checklist item, COAs determine whether a study’s data will withstand regulatory scrutiny and real-world validation.

At the same time, they’re also more complex. Trials increasingly involve global sites, digital data capture (eCOA platforms), and rare or heterogeneous populations. These factors make it more challenging to choose the right assessment.

It’s no surprise that 91% of researchers said they wanted to deepen their understanding of COA selection, according to findings released by Pearson Research in a recent report, “Unlocking the Power of COAs in Clinical Research.”

This growing complexity also reveals where many studies run into avoidable challenges.

Understanding COA pitfalls

Research findings reveal the three areas that cause the greatest COA friction: licensing, translations, and rater training. Each area points to missed opportunities for earlier alignment and stronger process design.

Licensing friction

When it comes to licensing, the findings show where researchers struggle most: 65% with slow turnaround, 64% with unclear usage terms, and 53% with licensing complexity or cost.

Many times, these challenges are compounded by poor timing. As one researcher put it, “We only discover licensing issues once it’s too late.”

This often leads to downstream delays that affect other areas of COA execution.

Translation gaps

Researchers struggle most with the availability of high-quality translations (64%) and the regulatory acceptance of translated COAs (63%). These challenges can delay global execution and increase the risk of retranslation or rejection.

Inconsistent rater training

Rater training is a sticking point, with more than half (52%) of researchers citing the lack of centralized tracking as a struggle, followed by limited expert access (45%) and budget constraints (40%). Inadequate training can threaten data quality, particularly in global or multisite trials. 

Additional friction points

Beyond these three core areas, researchers also report ongoing challenges with vendor coordination (60%) and confusion regarding copyright ownership (59%), further evidence that fragmented processes create unnecessary delays and uncertainty.

Core best practice: Act earlier, partner smarter

These recurring challenges point to a clear takeaway: Researchers need more runway to manage the details and avoid costly mistakes. However, as the Pearson report found, only 39% of researchers selected COAs during the early stages of protocol development.

This is where two best practices can have an immediate effect.

#1: Act earlier

Early COA planning supports faster approvals, clearer licensing, and stronger regulatory alignment. By integrating COA considerations at the protocol stage rather than after design is finalized, teams can prevent many of the delays and rework issues reported across licensing, translation, and training.

#2: Partner smarter

Connecting selection, licensing, and translation workflows from the outset reduces rework, enhances data integrity, and fosters stakeholder confidence. For example, 60% of researchers said access to expert consultation on endpoint or COA selection would make licensing easier. This is a key need, since licensing remains the top operational challenge.

Together, acting earlier and partnering smarter form the foundation for more precise, efficient, and patient-centered COA execution.

A checklist for improved COA selection

Effective COA selection depends on early planning and the right expertise; however, other hallmarks determine whether a measure truly delivers reliable and meaningful data.

Lynsey Psimas, Ph.D., Pearson Clinical Assessments, Pharma Division, director of business development for Pearson Research, offers this guidance:

“Effective COA selection begins with scientific alignment: The measure must map directly to the trial’s concept of interest and anticipated mechanism of action. Strong content validity, sensitivity to change, and psychometric rigor across the target population are all critical.” 

“Researchers should also evaluate administration feasibility, rater consistency, and cultural adaptability to ensure data integrity across global trials,” she adds.

Building on that foundation, include the following hallmarks of effective COA selection:

• Involve key stakeholders. Patients, caregivers, and clinicians help clarify what “meaningful change” truly looks like.
• Address burden early. Go beyond feasibility to ensure assessments are practical for participants and study teams.
• Prioritize rater support. Provide structured training and ongoing consistency checks to maintain reliability throughout the trial.
• Plan for global readiness. Identify translation and cultural needs early to ensure consistency across study sites.

There are also success factors that can be easy to overlook, details that often make the difference in data quality and interpretability:

• Check for range limits. Ensure the COA can detect both improvement and decline across the full range of function.
• Plan for repeat testing. Use alternate forms (such as those in RBANS) to minimize learning effects.
• Define meaningful change early. Decide what level of improvement represents true clinical benefit, not just a numerical shift.
• Think digital. Confirm that eCOA versions perform equivalently and are intuitive for raters and participants.
• Maintain a clear evidence trail. Document validation, training, and scoring decisions to support regulatory readiness.

Ultimately, effective COA selection depends on precision and foresight — aligning each measure not only with the science of the trial but with the experiences it aims to capture.

Partner for smarter COA decisions

The right partner helps teams apply COA best practices with clarity and confidence. Pearson supports researchers with validated tools and expert guidance across endpoint selection, licensing, and implementation, helping ensure each measure captures meaningful, reliable outcomes.

To explore deeper findings on COA selection, download the full research report, “Unlocking the Power of COAs in Clinical Research.”