Optimal Exact Tests for Multiple Binary Endpoints
Description
Calculates exact hypothesis tests to compare a treatment and a reference group with respect to multiple binary endpoints.
The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative
hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate
permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated
that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal
significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved
by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple
testing procedures.