In power and sample size calculations, and minimum detectable effect analyses for GEE, I have often seen an effective sample size reduction used to account for correlation. The idea is that the more correlated certain clusters are, like labs within a patient, or people within a town, the less precision you have to measure an effect. The effective sample size reduction is given as a constant scalar (less than 1) that multiplies the power expression for independent data. So the n is reduced by a constant, the power by a constant, and the minimum detectable effect (by the inverse of) a constant.
Is this a rigorous approach? Is there an accepted method for calculating this value? Has it been discussed in the literature? Does it matter if the modeling approach is linear, logistic, or survival models?