Sample size for comparing linear growth curves

Assuming a linear growth curve model under a suitable link function, we compute the sample size for comparing two treatment groups when the repeated measurements marginally follow exponential family distributions. From the treatment profiles of the chosen link function, we compute the common intercept beta0 and the regression slopes beta1 and beta2 to define delta = beta1 - beta2, the difference to be detected, under a specified alternative hypothesis. The dispersion matrices of the generalized estimating equations estimators are obtained under the null and alternative hypotheses using a suitable working correlation matrix. We compute the sample size assuming that delta is asymptotically normal. Details are worked out for repeated measures designs with binary and count data along with numerical examples.