Fig 2.

Backward stepwise logistic regression model selection for predicting concomitant Alzheimer’s Disease (AD) pathology in N = 127 cases (Training set) with a clinicopathologic diagnosis of PD or DLB from Penn. Concomitant AD pathology is defined as an AD Neuropathological Change (ADNC) score of Intermediate or High. a Akaike information criterion (AIC, left axis) at each step during model selection and the corresponding area under the receiver operating characteristics curve (AUC, right axis), estimated by ten-fold cross-validation, within the Training set are shown. Initial model included all AD risk SNPs, sex, and age at disease onset as predictors; sequential elimination of predictors and effect on AIC and AUC are shown from left to right. As the Training set cases showed no genetic variability at the TREM2 locus, this locus was not included in the model. b Coefficients (β), standard error (SE), and p-values for the four predictors included in the best model (lowest AIC) for predicting concomitant AD in LBD cases.