Control of familywise errors in multiple endpoint assessments via stepwise permutation tests.

We describe permutation based sequentially rejective multiple comparison procedures useful in multiple endpoint assessments. We used Monte Carlo methods to compare the power of these newly devised tests to that of tests due to Holm and Rom as well as to the classical Bonferroni method. We illustrate applications of the methods with analysis of visual field data collected from optic neuritis patients. We conclude that the new methods are particularly useful when there are many endpoints involved, the data are significantly correlated, and/or the distributional assumptions are questionable.