Research

Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies

FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence

Digital Twins as Synthetic Controls in Single-Arm Trials

Assessment of AI-generated digital twin methodology on reduction of treatment effect variance and potential clinical trial sample size saving using a Phase 2 trial dataset from patients with Alzheimer’s disease

Digital Twin Generators for Disease Modeling

Enhancing Longitudinal Clinical Trial Efficiency with Digital Twins and Prognostic Covariate-Adjusted Mixed Models for Repeated Measures (PROCOVA-MMRM)

Bayesian Prognostic Covariate Adjustment With Additive Mixture Priors

Addressing Concerns About Digital Twins in Clinical Trials

Neural Boltzmann Machines

Evaluating Digital Twins for Alzheimer’s Disease using Data from a Completed Phase 2 Clinical Trial

Prognostic Covariate Adjustment for Binary Outcomes Using Stratification

Restricted mean survival time estimate using covariate adjusted pseudovalue regression to improve precision

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

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

Bayesian prognostic covariate adjustment

Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks

Machine learning for comprehensive forecasting of Alzheimer’s Disease progression

A high-bias, low-variance introduction to Machine Learning for physicists