  The incidence of Alzheimer's disease (AD) and other forms of dementia is
increasing in most western countries. For a precise and early diagnosis,
several examination modalities exist, among them single-photon emission
computed tomography (SPECT) and the electroencephalogram (EEG). The latter is
highly available, free of radiation hazards, and non-invasive. Thus, its
diagnostic utility regarding different stages of dementia is of great interest
in neurological research, along with the question of whether its utility
depends on age or sex of the person being examined. However, SPECT or EEG
measurements are intrinsically multivariate, and there has been a shortage of
sufficiently general inferential techniques for the analysis of multivariate
data in factorial designs when neither multivariate normality nor equality of
covariance matrices across groups should be assumed. We adapt an asymptotic
model based (parametric) bootstrap approach to this situation, demonstrate its
ability, and use it for a truly multivariate analysis of the EEG and SPECT
measurements, taking into account demographic factors such as age and sex.
These multivariate results are supplemented by marginal effects bootstrap
inference whose theoretical properties can be derived analogously to the
multivariate methods. Both inference approaches can have advantages in
particular situations, as illustrated in the data analysis.
