  In covariate-adaptive or response-adaptive randomization, the treatment
assignment and outcome can be correlated. Under this situation,
re-randomization tests are a straightforward and attractive method to provide
valid statistical inference. In this paper, we investigate the number of
repetitions in the re-randomization tests. This is motivated by the group
sequential design in clinical trials, where the nominal significance bound can
be very small at an interim analysis. Accordingly, re-randomization tests lead
to a very large number of required repetitions, which may be computationally
intractable. To reduce the number of repetitions, we propose an adaptive
procedure and compare it with multiple approaches under pre-defined criteria.
Monte Carlo simulations are conducted to show the performance of different
approaches in a limited sample size. We also suggest strategies to reduce total
computation time and provide practical guidance in preparing, executing and
reporting before and after data are unblinded at an interim analysis, so one
can complete the computation within a reasonable time frame.
