Interaction

Abstract

In Chap. 25 interaction of continuous outcome data has been assessed. The efficacy of one treatment is better in one of the subgroups, and the efficacy of the other treatment is better in the other subgroup. With interaction an overall data analysis is pretty meaningless, and separate analyses of the subgroups must be presented. Of course, with multiple treatments and subgroups interaction is possible as well, but the analysis is much more complex. In the current chapter we will demonstrate how to assess interaction of binary outcome data instead of continuous outcome data. A two-sample t-test (or rather z-test) (see also Chap. 36) can be used to test whether the differences in treatment efficacies of two subgroups are significantly different from one another. The example given shows that a significant interaction between genders can be demonstrated in such data. In the males the treatment 1 performed better, in the females the treatment 2 did so.