ADSS: A Composite Score to Detect Disease Progression in Alzheimer’s Disease
Guogen Shan, Xinlin Lu, Zhigang Li, Jessica Caldwell, Charles Bernick, Jeffrey L. Cummings
Abstract
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: election (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.