In survival analysis, meaningful and easy-to-interpret effect estimands play an important role to compare the survival times of different groups, e.g. treatment vs. placebo. However, there is a lack of inference methods for complex factorial survival designs and competing risks data. Therefore, we develop flexible hypothesis tests for this framework based on resampling theory, which also allow for simultaneous testing of multiple hypotheses.