When there is interference, a subject's outcome depends on the treatment of others and treatment effects may take on several different forms. This situation arises often, particularly in vaccine evaluation. In settings where interference is likely, two-stage cluster randomized trials have been suggested as a means of estimating some of the causal contrast of interest. Working in the finite population setting to investigate rare and unplanned subgroup analyses using some of the estimators that have been suggested in the literature, include Horvitz-Thompson, Hajek, and what might be called the natural extension of the marginal estimators suggested in Hudgens and Halloran 2008. I define the estimands of interest conditional on individual, group and both individual and group baseline variables, giving unbiased Horvitz-Thompson style estimates for each. I also provide variance estimators for several estimators. I show that the Horvitz-Thompson (HT) type estimators are always unbiased provided at least one subject within the group or population, whatever the level of interest for the estimator, is in the subgroup of interest. This is not true of the "natural" or the Hajek style estimators, which will often be undefined for rare subgroups.