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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 biomarkers to predict MCI to AD conversion over 12 months was also assessed. Although originally optimized to discriminate across diagnostic categories, the ADAS. All, weighted according to the RF model, did nearly as well or better than individual or composite baseline neuroimaging or CSF biomarkers in prediction of 12-month conversion from MCI to AD. These suggest that a modified subscore weighting scheme applied to the 13-item ADAS-cog is comparable to imaging or CSF markers in prediction of conversion from MCI to AD at 12 months.