Public Health

Area of Science:

• Neurology
• Clinical Trials
• Biostatistics

Background:

• Alzheimer's dementia (AD) poses a growing public health challenge with aging populations.
• Many AD clinical trials fail due to insufficient statistical power and low event rates.
• Current enrollment methods using brain imaging exclude diverse populations, leading to unrepresentative samples.

Purpose of the Study:

• To propose a novel predictive model for enrolling high-risk, cognitively normal individuals in AD clinical trials.
• To enhance the efficiency and representativeness of AD clinical trial participant selection.
• To address the limitations of current enrollment strategies that systematically exclude certain demographic groups.

Main Methods:

• Developed a longitudinal factor model utilizing cognitive test scores to predict AD conversion within three years.
• Employed data from the National Alzheimer's Coordinating Center for model development.
• Used simulation studies to assess the model's performance in hypothetical clinical trials.

Main Results:

• The predictive model significantly improves statistical power for AD clinical trials.
• The model demonstrates a reduction in the required sample size across various drug effect scenarios.
• Compared to other selection methods, the proposed model offers substantial statistical advantages.

Conclusions:

• Predictive modeling using cognitive test scores can improve AD clinical trial enrollment efficiency.
• The novel factor model facilitates the inclusion of diverse participant groups.
• This approach promotes better representation of underrepresented populations in AD research.