Model-based statistical approaches were used to compare the ability of the Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog), cerebrospinal fluid (CSF), fluorodeoxyglucose positron emission tomography and volumetric magnetic resonance imaging (MRI) markers to predict 12-month progression from mild cognitive impairment (MCI) to Alzheimer disease (AD). Using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data set, properties of the 11-item ADAS-cog (ADAS.11), the 13-item ADAS-cog (ADAS.All) and novel composite scores were compared, using weighting schemes derived from the Random Forests (RF) tree-based multivariate model. Weighting subscores using the RF model of ADAS.All enhanced discrimination between elderly controls, MCI and AD patients. The ability of the RF-weighted ADAS-cog composite and individual scores, along with neuroimaging or biochemical…
Home Alzheimer's Research Derivation of a New ADAS-cog Composite Using Tree-based Multivariate Analysis: Prediction of...
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