Consider a longitudinal study designed to estimate the difference in the rates of change in some outcome between two different groups. In this case, the variance of the estimator depends on several factors, including the variability in the outcome, the amount of missing data due to dropout, the distribution of additional covariates, and the degree and structure of the within-unit correlation across time. Although it is often possible to compute the variance (or an approximation to it) directly from a mathematical formula, this can be unwieldy for those unfamiliar with such computations. In this presentation, I will demonstrate (using real examples) how -xtgee- can be used to compute the variance, from which an estimate of power may be obtained. By creating an appropriate pseudo-dataset, it is possible to specify virtually any covariate distribution and pattern of dropout. In addition, because -xtgee- will accept an arbitrary fixed correlation matrix, it is easy to specify whatever correlation structure is considered most plausible. This method is intuitive, and makes it easy for researchers to explore the effects that changes in their assumptions have on a study's power. A comparison of the results of this method with those generated by other sample size software will also be presented.