Using Digital Twins to Decrease Enrollment and Increase Statistical Power in Alzheimer's Disease Trials (CTAD 2020)

Background: Drug development for Alzheimer’s disease (AD) is increasingly expensive and time-consuming.  To decrease the high failure rate of these trials, it will be necessary to improve clinical trial design by reducing total trial size and/or recruitment time.  Randomized controlled trials (RCTs) have long been the gold-standard among clinical trial designs, even though they can be very inefficient. The volume of clinical trials provides an opportunity to improve the efficiency of AD trials, which has been highlighted by the FDA in a number of communications. With data collected from the control groups of many prior AD trials and state-of-the-art statistical methods, we have developed machine learning (ML) technology to comprehensively model the progression of control subjects. Our model can generate digital twins, which are digital subject records generated from the baseline data of actual subjects in a trial. These digital twins show the potential outcomes of individual subjects had they received a placebo.

Enter your email address to download paper.

Click the link to begin download.
Oops! Something went wrong while submitting the form.

Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins


Bayesian prognostic covariate adjustment


Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score

Linear adjustment for a prognostic score is an effective and safe method for leveraging historical data to reduce uncertainty in randomized trials.
Our novel method - Bayesian prognostic covariate adjustment - is a Bayesian analysis that draws on the strengths of the prognostic model approach.
Here, we have demonstrated that a particular type of generative model (i.e., CRBMs) can be used to accurately model disease progression for patients with MCI or AD.