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While many overall tests have been proposed, very few have power and sample size methods. We develop accurate power and sample size methods and software to facilitate study planning for high\u2010dimensional pathway analysis. With an account of any complex correlation structure between high\u2010dimensional outcomes, the new methods allow power calculation even when the sample size is less than the number of variables. We derive the exact (finite\u2010sample) and approximate non\u2010null distributions of the \u2018univariate\u2019 approach to repeated measures test statistic, as well as power\u2010equivalent scenarios useful to generalize our numerical evaluations. Extensive simulations of group comparisons support the accuracy of the approximations even when the ratio of number of variables to sample size is large. We derive a minimum set of constants and parameters sufficient and practical for power calculation. Using the new methods and specifying the minimum set to determine power for a study of metabolic consequences of vitamin B6 deficiency helps illustrate the practical value of the new results. Free software implementing the power and sample size methods applies to a wide range of designs, including one group pre\u2010intervention and post\u2010intervention comparisons, multiple parallel group comparisons with one\u2010way or factorial designs, and the adjustment and evaluation of covariate effects. 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