Abstract
The graph-based approach to multiple testing is an intuitive method that enables a study team to represent clearly, through a directed graph, its priorities for hierarchical testing of multiple hypotheses, and for propagating the available type-1 error from rejected or dropped hypotheses to hypotheses yet to be tested. Although originally developed for single-stage nonadaptive designs, we show how it may be extended to two-stage designs that permit early identification of efficacious treatments, adaptive sample size re-estimation, dropping of hypotheses, and changes in the hierarchical testing strategy at the end of stage one. Two approaches are available for preserving the familywise error rate in the presence of these adaptive changes, the p value combination method, and the conditional error rate method. In this investigation, we will present the statistical methodology underlying each approach and will compare the operating characteristics of the two methods in a large simulation experiment.