My study design is as follows:   N subjects received MRI examinations. In a subset of cases, one or more sequences (i.e. separate “components” of the examination) were of insufficient image quality, e.g. due to subject motion, and therefore reacquired. For all sequences with >1 acquisitions, exactly one was selected for further use solely based on visual impression (even if all acquisitions were quite “bad”, one of them still had to be selected). For all acquisitions there is also a set of quantitative (continuous) quality parameters. Sample size: 80-400 subjects per sequence Acquisitions: 1-4, most often 2 Parameters: 3-10 continuous variables per sequence (e.g. sharpness, noise, signal-to-noise ratio) I want to predict the binary outcome of an acquisition being selected or not selected for use (dependent variable) based on the quantitative quality parameters (multiple independent predictor variables, within-subject comparison). I find it complex because acquisitions were not assessed and categorized individually, but always in comparison to at least one more acquisition of the same sequence within a subject. These acquisitions „mutually exclude“ one another, since only one of them could be selected for further use.   So far I'm thinking this should be approached as a repeated-measures problem using a mixed-effects model.   I would use the following to assess any parameter individually:  proc mixed data=all; class id; model selected=param1; repeated /subject=id; where sequence="seq1"; run;   But what I'm really interested in is whether or not I can predict the selection from looking at all parameters together. Does is therefore make sense to simply write: ... model selected=param1*param2*param3*...*paramn; ...   It probably doesn't, but I would be grateful if someone could guide me in the right direction. ... View more