Two-stage response adaptive randomization designs for multi-arm trials with binary outcome

ADSS: A Composite Score to Detect Disease Progression in Alzheimer's Disease

Background

Composite scores have been increasingly used in trials for Alzheimer's disease (AD) to detect disease progression, such as the AD Composite Score (ADCOMS) in the lecanemab trial.

Objective

To develop a new composite score to improve the prediction of outcome change.

Methods

We proposed to develop a new composite score based on the statistical model in the ADCOMS, by removing duplicated sub-scales and adding the model selection in the partial least squares (PLS) regression.

Results

The new AD composite Score with variable Selection (ADSS) includes 7 cognitive sub-scales. ADSS can increase the sensitivity to detect disease progression as compared to the existing total scores, which leads to smaller sample sizes using the ADSS in trial designs.

Conclusions

ADSS can be utilized in AD trials to improve the success rate of drug development with a high sensitivity to detect disease progression in early stages.