It aims at processing a group analysis while preserving individual information and at overcoming as far as possible limitations of the spatial normalization used to compare different subjects.
We then developed our matrix to compare these individual metrics, and using this matrix, we compared different subjects to one another.
One should note that this study did not compare quality of life of the same individuals at several time intervals but compared different subjects with various time elapsed since diagnosis.
Altman, for example, stated that "most clinical trials yield data of this type, as do observational studies comparing different groups of subjects" ([ 11], p. 191).
This entails a problem when comparing different meals administered to subjects with different iron status and different iron absorption capacity, and thus there was an informal agreement on normalising iron absorption results to the 40%% absorption from a reference dose of iron.
However, the aim of this trial was to assess CIMT progression in subjects with type 2 diabetes treated according to current guidelines and to possibly identify early markers predictive of atherosclerosis progression rather than comparing different treatment targets in these subjects.
The results of the rmANOVA revealed that the within-subject effect (comparing different states of natalizumab use) was highly significant (F = 38.665, P < 0.001).
However, the detection of breast abnormalities can be enhanced by comparing different images of the same subject, either from the same breast at different time points [ 16] or by asymmetry analyses of the left and right breasts [ 16, 17].
An experimental design comparing different instruments measuring replicate breath samples from several subjects is presented here.
Crucially, this Bayesian scheme can be applied to empirical behavioral data, allowing one to compare different models of subject responses and quantify their trial-by-trial estimates of states of the environment that lead to sensory predictions, including the precision of these estimates.
Regarding specific EEG correlates of RT performance, Jokeit and Makeig [ 9] compared different EEG patterns of subjects with quick and slow mean RT.
