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.

翻译：在平台试验中，利用非并发对照分析后期加入的实验组近来在学术和监管层面均受到广泛关注。虽然整合这类数据能提升统计功效并降低所需样本量，但若试验存在时间漂移，也可能给效应估计量引入偏倚。为减轻潜在的时间日历偏倚，我们提出了多种基于频率学派模型的方案，在利用非并发对照数据的同时对时间趋势进行调整。目前一种可用的频率学派模型将时间作为分类固定效应，将试验持续时间划分为若干时段——这些时段定义为任意治疗组进入或离开试验平台的时间区间。本研究提出该模型的两种扩展方案：其一，通过将试验分割为固定长度的日历时间区间，考虑时间协变量的替代定义；其二，提出调整时间趋势的新方法。具体而言，我们探究了通过调整自相关随机效应来考虑相邻时间区间之间的依赖性，以及采用样条回归以平滑多项式函数对时间建模。我们通过模拟研究评估了所提方法的性能，并借助案例研究展示了其应用。