What if you could predict placebo responsiveness of each patient in your clinical trial? What if this factor was the difference that reduced data variability and increased study power, so you can get therapies to patients faster? By combining machine learning with patient psychology data – you can.

The placebo response has plagued drug development for decades, causing trial and program failure, increasing development costs and delaying delivery of drugs to patients. Historical strategies to address the placebo response may help but haven't completely solved the placebo response problem.

This is because the quantitative impact of patient psychology on study data has been missing. Until now. In order to fully address the placebo response, unique characteristics of individual patients – like their psychology, perceptions and beliefs – must be considered. But how?

Advanced methods like AI and machine learning are uniquely poised to help scientists uncover the full spectrum of patient placebo responsiveness in a clinical trial. Learn more about this approach by attending our webinar, which explains how a solution like Placebell©™ leverages a time-tested predictive algorithm to improve clinical trial assay sensitivity and de-risk drug development.

In this webinar, you will learn:

• The “missing link” to understanding placebo response in clinical trial patients
• The role of machine learning in addressing the complexity of the placebo response
• How to de-risk drug development and improve clinical trial assay sensitivity using Placebell©™