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Table 4 Information gain of multivariable logistic regression models compared to univariate logistic regression including only amyloid modalities

From: PET and CSF amyloid-β status are differently predicted by patient features: information from discordant cases

  AIC AIC 
PredictorPET ~
CSF
PET ~
CSF + predictor
AIC differenceCSF ~
PET
CSF ~
PET + predictor
AIC difference
TotalAPOE E4 positivity5805334757353142
CSF tau5805087157356310
CSF p-tau5804819957355517
SCDAPOE E4 positivity142138416715215
CSF tau142132101671625
CSF p-tau142132101671634
MCIAPOE E4 positivity10486181041004
CSF tau1047430104105− 1
CSF p-tau1048321104106− 2
DementiaAPOE E4 positivity3172952228626719
CSF tau317294232862852
CSF p-tau3172635428627610
  1. This table illustrates the change in Akaike Information Criterion (AIC) from the bivariate models including only amyloid modalities (PET ~ CSF and CSF ~ PET) to multivariable models including also an additional predictor. AIC measures model fit and penalizes adding additional predictors. A decrease in AIC between models shows some (0–2), considerable (4–7), or strong (> 10) evidence for gain in model fit for the second model