The analysis of platform trials can be enhanced by utilizing non-concurrent controls. Since including this data might also introduce bias in the treatment effect estimators if time trends are present, methods for incorporating non-concurrent controls adjusting for time have been proposed. However, so far their behavior has not been systematically investigated in platform trials that include interim analyses. To evaluate the impact of an interim analysis in trials utilizing non-concurrent controls, we consider a platform trial featuring two experimental arms and a shared control, with the second experimental arm entering later. We focus on a frequentist regression model that uses non-concurrent controls to estimate the treatment effect of the second arm and adjusts for time using a step function to account for temporal changes. We show that performing an interim analysis in Arm 1 may introduce bias in the point estimation of the effect in Arm 2, if the regression model is used without adjustment, and investigate how the marginal bias and bias conditional on the first arm continuing after the interim depend on different trial design parameters. Moreover, we propose a new estimator of the treatment effect in Arm 2, aiming to eliminate the bias introduced by both the interim analysis in Arm 1 and the time trends, and evaluate its performance in a simulation study. The newly proposed estimator is shown to substantially reduce the bias and type I error rate inflation while leading to power gains compared to an analysis using only concurrent controls.

翻译：利用非并发对照可增强平台试验的分析。由于在存在时间趋势的情况下纳入此类数据也可能引入治疗效应估计的偏倚，已提出调整时间的非并发对照纳入方法。然而，迄今为止，在包含中期分析的平台试验中，其行为尚未得到系统研究。为评估中期分析在采用非并发对照的试验中的影响，我们考虑一个包含两个试验组和一个共享对照组的平台试验，其中第二个试验组较晚加入。我们聚焦于一种频率学派回归模型，该模型利用非并发对照估计第二组的治疗效应，并采用阶梯函数调整时间以反映时间变化。研究表明，若未调整即使用回归模型，对第一组进行中期分析可能引入第二组效应点估计的偏倚，并探讨边际偏倚及基于第一组在中期后继续进行的条件偏倚如何随不同试验设计参数变化。此外，我们提出了第二组治疗效应的新估计量，旨在消除第一组中期分析和时间趋势共同引入的偏倚，并通过模拟研究评估其性能。与仅使用并发对照的分析相比，新提出的估计量显著降低了偏倚和I类错误率膨胀，同时提高了检验效能。