New Study Boosts Trial Precision in Measuring Pain, Mood, and Fatigue

Published in the Journal of Pain, Cognivia's research brings more objectivity to pain trials, leading to cleaner data, smarter decisions, and better outcomes 

MONT-SAINT-GUIBERT, Belgium, Sept. 4, 2025 /PRNewswire/ -- Pain, mood, and fatigue are among the hardest outcomes to measure in clinical research, largely due to their highly subjective nature. A newly published study in *The Journal of Pain * offers a simple method to efficiently perform a covariate adjustment that significantly improves how these "high-variability" endpoints are analyzed.

In the peer-reviewed study, the authors provide guidance on how to use composite baseline covariates to comply with FDA guidance and to optimize data analysis. In one real-world Phase III acute lumbar pain case study, selecting and building prognostic covariates, based on patient factors, increased trial precision. Additionally, when researchers applied composite psychological predictors from Cognivia's Placebell platform, results improved even further up to 23.4%. Placebell automates the creation of these predictors, making this approach scalable and repeatable across studies, giving it broad implications beyond pain trials.

"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."

Covariate adjustment is a regulator-supported method that accounts for differences between patients, such as psychological or baseline traits, to reduce noise in outcomes like pain, mood, or fatigue. Though backed by FDA guidance, covariate adjustment remains underused. Cognivia is the first life sciences technology company to offer a practical roadmap for implementing covariate adjustment in real-world trials, with guidance that is easy to apply and proven effective across three separate studies. While demonstrated in a pain trial, the approach applies broadly to any study with subjective or high-variability endpoints, including not only studies around the central nervous system, fatigue, or conditions involving emotional or mental health symptoms but also in many other therapeutic areas and indications.

"This approach is a game changer for trials with subjective endpoints displaying a high variability," said Samuel Branders, Cognivia's Director of Data Science and co-author of the study. "It helps produce clear, more trustworthy results and makes better use of patient resources by increasing precision without inflating sample size."

The study titled, From theory to practice: Simple rules for improving clinical trial confidence with covariate adjustment, was published in the September 2025 edition of the Journal of Pain. Additional authors include Arthur Ooghe, Alvaro Pereira, Luana Colloca, Elizabeth Standard, Chris Ambrose, and Dmitri Lissin.

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About Cognivia
Cognivia is the first and only company to combine quantification of patient psychology with artificial intelligence / machine learning (ML) to improve measurement of therapeutic efficacy in clinical trials and beyond. Cognivia technologies predict patient behavior and treatment response in clinical trials using predictive ML powered algorithms based on quantitative understanding of patient psychological traits, expectations and beliefs collected via our own and specific questionnaires developed toward that objective. Cognivia aims at harnessing "the power of the mind" and quantifying this unique phenomenon to improve clinical trial success rates, de-risk drug development and ultimately improve healthcare.

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SOURCE Cognivia