New Publication in The Journal of Pain Demonstrates Scalable Method to Improve Measurement of Subjective Endpoints in Clinical Trials

Mont-Saint-Guibert, Belgium — September 4, 2025 — In an era where clinical trial failure rates remain high, not due to ineffective therapies but due to noisy data, Cognivia’s latest research offers a transformative solution. A newly published study in The Journal of Pain presents a validated, scalable method to improve precision in trials measuring pain, mood, and fatigue—outcomes notoriously difficult to quantify.

The study, titled From theory to practice: Simple rules for improving clinical trial confidence with covariate adjustment, was co-authored by Cognivia’s data science team, Scilex Holding CEO Dmitri Lissin and Key Opinion Leader Luana Colloca. It introduces a practical approach to covariate adjustment, a regulator-endorsed method for reducing variability in subjective endpoints by accounting for patient-specific baseline traits.

“Trials too often fail, not because therapies are ineffective, but because the signals get lost in noise,” said Dominique Demolle, PhD, CEO and Co-Founder of Cognivia. “This study shows a clear, validated path for tackling that noise, without additional patients, delays or cost.”

In a real-world Phase III acute lumbar pain trial, applying composite baseline covariates improved precision. When researchers added psychological predictors generated by Cognivia’s Placebell platform, results improved even further—by up to 23.4%. Placebell automates the creation of these predictors, making the method repeatable and scalable across studies.

This approach aligns with the FDA’s May 2023 guidance on covariate adjustment, which encourages sponsors to use prognostic baseline covariates to improve statistical efficiency in randomized trials. Cognivia’s method is among the first to translate this guidance into a practical, real-world framework.
🔗 FDA Guidance: Adjusting for Covariates in Randomized Clinical Trials

“This approach is a game changer for trials with subjective endpoints,” said Samuel Branders, Director of Data Science at Cognivia. “It helps produce clear, more trustworthy results and makes better use of patient resources by increasing precision without inflating sample size.”

While demonstrated in pain research, the method has broad implications for any trial involving high-variability endpoints, including CNS disorders, fatigue, emotional and mental health conditions, and other therapeutic areas.

About Cognivia: Cognivia empowers clinical trial sponsors to make smarter, more confident decisions by reducing behavioral variability, one of the biggest drivers of trial failure. The company’s AI-powered behavioral models predict placebo responders, dropout and nonadherence risk, and engagement patterns to improve statistical power and reliability. These insights result in fewer trial delays, increased drug accuracy, and improved success rates.