Guogen Shan, Xinlin Lu, Zhigang Li, Jessica Z K Caldwell, Charles Bernick, Jeffrey Cummings
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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.
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