The problems of analyzing data from large clinical trials are considered. Specifically, variable selection, lack of independence, analysis of repeated observations, alternate measurements of similar phenomena, and exclusion of variables unrelated to treatment are considered in the context of multivariate data obtained in a clinical therapy trial in multiple sclerosis (MS).The criteria considered are: 1) Dropping variables having low “signal to noise” ratio 2) Maximizing prediction 3) Maximizing separation between treatment groups, and 4) Reducing the dimension of multivariate data. Analyses are described and illustrated in the selection of variables from the ACTH clinical trail. The selection of “important” variables was validated by means of split-half analyses. The analytical approach, which has general application, and the implications of the analysis on the reduced data set are discussed.
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