The overall risk of clinical trial failure is still too high – meaning more repeat trials, lost timelines and premature abandonment of programs. In part, this is because of the placebo response: the measured improvement of a patient in a trial after receiving a sham treatment. While a high placebo response doesn’t necessarily mean the treatment doesn’t work, the resulting data variability generates noise and obscures the efficacy signal. So, trials fail. 

What can be done?

Historically, the industry has used methods ranging from excluding placebo responders to training patients to reproducibly report symptoms. Unfortunately, the impact of these methods falls short, especially as the placebo response continues to increase.

That’s why, in this Applied Clinical Trials article, Cognivia CBO Erica Smith, PhD, offers a new, innovative approach: using predictive models based on machine learning and patient psychology data 

Learn more about the prevalence of placebo response in clinical trials, the history of how trials have addressed it to date, and this new approach to reduce the risk of trial failure in the full article.