New Cognivia article in Applied Clinical Trials on “Using Machine Learning to Predict Placebo Response and Increase Clinical Trial Success”

February 9, 2022

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

From covariates to confounding factors: the danger of having too many covariates

October 20, 2020

Clinical trials typically evaluate efficacy of experimental therapies in heterogeneous patient populations, as patient characteristics vary significantly. These patient characteristics might be prognostic factors that ultimately induce variability in clinical trial data. An imbalance in these factors between treatment groups at baseline will increase variability of the estimated treatment effect, ultimately compromising study power and decreasing

Correcting For The Individual Patient Regression To The Mean Effect

August 7, 2019

Often, the primary endpoint of RCTs is defined as a change from baseline of a continuous outcome. In
such cases, regulators recommend including the outcome’s baseline value as a covariate in the
statistical analysis. Regression to the mean can explain the benefits of this procedure.