  For indications where only unstable reference treatments are available and
use of placebo is ethically justified, three-arm `gold standard' designs with
an experimental, reference and placebo arm are recommended for non-inferiority
trials. In such designs, the demonstration of efficacy of the reference or
experimental treatment is a requirement. They have the disadvantage that only
little can be concluded from the trial if the reference fails to be
efficacious. To overcome this, we investigate novel single-stage, adaptive test
strategies where non-inferiority is tested only if the reference shows
sufficient efficacy and otherwise $\delta$-superiority of the experimental
treatment over placebo is tested. With a properly chosen superiority margin,
$\delta$-superiority indirectly shows non-inferiority. We optimize the sample
size for several decision rules and find that the natural, data driven test
strategy, which tests non-inferiority if the reference's efficacy test is
significant, leads to the smallest overall and placebo sample sizes. We proof
that under specific constraints on the sample sizes, this procedure controls
the family-wise error rate. All optimal sample sizes are found to meet this
constraint. We finally show how to account for a relevant placebo drop-out rate
in an efficient way and apply the new test strategy to a real life data set.
