  Utilizing non-concurrent controls in the analysis of late-entering
experimental arms in platform trials has recently received considerable
attention, both on academic and regulatory levels. While incorporating this
data can lead to increased power and lower required sample sizes, it might also
introduce bias to the effect estimators if temporal drifts are present in the
trial. Aiming to mitigate the potential calendar time bias, we propose various
frequentist model-based approaches that leverage the non-concurrent control
data, while adjusting for time trends. One of the currently available
frequentist models incorporates time as a categorical fixed effect, separating
the duration of the trial into periods, defined as time intervals bounded by
any treatment arm entering or leaving the platform. In this work, we propose
two extensions of this model. First, we consider an alternative definition of
the time covariate by dividing the trial into fixed-length calendar time
intervals. Second, we propose alternative methods to adjust for time trends. In
particular, we investigate adjusting for autocorrelated random effects to
account for dependency between closer time intervals and employing spline
regression to model time with a smooth polynomial function. We evaluate the
performance of the proposed approaches in a simulation study and illustrate
their use by means of a case study.
