  Adaptive designs(AD) are a broad class of trial designs that allow preplanned
modifications based on patient data providing improved efficiency and
flexibility. However, a delay in observing the primary outcome variable can
harm this added efficiency. In this paper, we aim to ascertain the size of such
outcome delay that results in the realised efficiency gains of ADs becoming
negligible compared to classical fixed sample RCTs.
  We measure the impact of delay by developing formulae for the no. of overruns
in 2 arm GSDs with normal data, assuming different recruitment models. The
efficiency of a GSD is usually measured in terms of the expected sample size
(ESS), with GSDs generally reducing the ESS compared to a standard RCT. Our
formulae measures the efficiency gain from a GSD in terms of ESS reduction that
is lost due to delay. We assess whether careful choice of design (e.g.,
altering the spacing of the IAs) can help recover the benefits of GSDs in
presence of delay. We also analyse the efficiency of GSDs with respect to time
to complete the trial.
  Comparing the expected efficiency gains, with and without consideration of
delay, it is evident GSDs suffer considerable losses due to delay. Even a small
delay can have a significant impact on the trial's efficiency. In contrast,
even in the presence of substantial delay, a GSD will have a smaller expected
time to trial completion in comparison to a simple RCT. Although the no. of
stages have little influence on the efficiency losses, the timing of IAs can
impact the efficiency of a GSDs with delay. Particularly, for unequally spaced
IAs, pushing IAs towards latter end of the trial can be harmful for the design
with delay.
