| Summary: | Abstract Introduction Clinical trials in Alzheimer's disease are aimed at early stages of disease, including preclinical Alzheimer's disease. The high cost and time required to screen large numbers of participants for Aβ pathology impede the development of novel drugs. This study's objective was to evaluate the extent to which inexpensive and easily obtainable information can reduce the number of screen failures by increasing the proportion of Aβ+ participants identified for screening. Methods We used random forest models to evaluate the positive predictive value of demographics, APOE, and longitudinal cognitive rates in the prediction of amyloid pathology, measured by florbetapir PET or cerebrospinal fluid. Results Predicting Aβ positivity with demographic, APOE, and cognitive information yielded a positive predictive value estimate of 0.65 (95% CI, 0.50–0.96), nearly a 60% increase over the reference Aβ+ prevalence in the cohort of 0.41. Conclusions By incorporating this procedure, clinical trial screening costs may be substantially reduced.
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