Synthetic Control Subjects for Alzheimer's Disease Clinical Trials (JSM 2019)

Objective: To develop a method to model disease progression that simulates detailed clinical data records for subjects in the control arms of Alzheimer's disease clinical trials.Methods: We used a robust data processing framework to build a dataset from a database of subjects in the control arms of a diverse set of 28 clinical trials on Alzheimer's disease. From this dataset, we selected 1908 subjects with 18-month trajectories of 44 variables and trained a probabilistic generative model called a Conditional Restricted Boltzmann Machine (CRBM) to simulate disease progression in 3-month intervals across all variables. Results: Based on a statistical analysis comparing data from actual and simulated subjects, the model generates accurate subject-level distributions across variables and through time. Focusing on a common clinical trial endpoint for Alzheimer's disease (ADAS-Cog), we show the model can accurately predict disease progression and may be used to model the control arm of a clinical trial whose data are distinct from the training and test datasets. Conclusion: The ability to simulate dozens of clinical characteristics simultaneously is a powerful tool to model disease progression. Such models have useful applications for clinical trials, from analyzing control groups to supplementing actual subject data in control arms.

Enter your email address to download paper.

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

CTAD 2020: Unlearn will present abstracts to demonstrate how Digital Twins enable smaller, more efficient trials

Blog

Announcing The Unlearn Opportunities Internship Program

Podcasts

UCSF Rosenman Institute - The Health Technology Podcast #59: Digital Twins for Clinical Trials

Charles Fisher: Digital Twins for Clinical Trials
Creating Opportunities for Students of Data Science and Business from Underrepresented Groups in STEM
Unlearn will present data from two separate abstracts demonstrating how Digital Twins can power novel trial designs and accelerate timelines.