Enhancing Alzheimer’s Disease Clinical Trials through Prognostic Score Covariate Adjustment

Music City Center 

Prognostic scores (PS) can improve statistical efficiency in clinical trials by reducing the variance of treatment effect estimates, leading to trial derisking and cost savings. We developed three PS's using 78 baseline covariates for the prediction of 18-month changes in the Clinical Dementia Rating Scale – Sum of Boxes (CDR-SB) using a harmonized cohort of patients with Alzheimer's disease (AD), pooled from both randomized clinical trials (RCT) and real-world databases (n = 1549). In an internal test set (n = 398), a stacked ensemble model achieved a Pearson correlation of 0.51 between predicted and observed 18-month CDR-SB changes. In a held-out RCT test set (n = 650), PS adjustment reduced treatment effect variance by 22%, yielding a power increase from 80% to 87% and an effective sample size increase of 20%. Simpler alternatives, such as a linear PS or treating baseline AD Assessment Scale-Cognitive (ADAS-Cog) as a PS, also provided meaningful variance reduction with additional benefits of improved clinical interpretability. Our findings support the use of PS to enhance statistical efficiency in clinical trials, and further validation on external datasets is recommended.

Covariate Adjustment

Prognostic Score

Clinical Trial Efficiency

Machine Learning

Alzheimer's Disease

Real World Data 

Biopharmaceutical Section