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Embracing Human Complexity in Clinical Research
As clinical trials become more diverse, patient heterogeneity, placebo responses, and inter-site variability become more pronounced, contributing to high rates of failed trials and extended development timelines…
Predicting drop-out in early-stage Type 1 Diabetes clinical trials to improve retention through Personalized Engagement Strategies
Background: Patient non-adherence and drop-out increase the time and cost of clinical trials. A tool that predicts, at…
Improving Precision of Clinical Trials Results in T1 Diabetes with Transferrable Prognostic Models
Background: In randomized controlled trials (RCTs), assay sensitivity issues can affect both statistical power and the confidence in…
Discussion on the Use of Placebell and FDA’s Feedback
Cognivia recently initiated a discussion with the FDA through the “Innovative Science and Technology Approaches for New Drugs (ISTAND) Pilot Program” process. A Letter of Intent (LOI) was…
Disruptive Technology Without the Disruption
Implementing an innovative technology within a clinical trial is about much, much more than the technology – and while the tech might be “disruptive,” its actual implementation…
A “Unik” Approach to Capturing and Managing Patient Data
How the drive to maximize valuable data while minimizing patient and study team burden resulted in a unique data collection and management platform. Patient engagement is a…
Predicting Patient Drop-Out in Clinical Trials: A First Step Toward Personalized Engagement Strategies
Objective: Patient non-adherence and drop-out significantly extend the duration and cost of clinical trials. A predictive tool identifying…
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