Time-trend impact on treatment estimation in two-arm clinical trials with a binary outcome and Bayesian response adaptive randomization

Clinical trial design and analysis often assume study population homogeneity, although patient baseline profile and standard of care may evolve over time, especially in trials with long recruitment periods. The time-trend phenomenon can affect the treatment estimation and the operating characteristics of trials with Bayesian response adaptive randomization (BRAR). The mechanism of time-trend impact on BRAR is increasingly being studied but some aspects remain unclear. The goal of this research is to quantify the bias in treatment effect estimation due to the use of BRAR in the presence of time-trend. In addition, simulations are conducted to compare the performance of three commonly used BRAR algorithms under different time-trend patterns with and without early stopping rules. The results demonstrate that using these BRAR methods in a two-arm trial with time-trend may cause type I error inflation and treatment effect estimation bias. The magnitude and direction of the bias are affected by the parameters of the BRAR algorithm and the time-trend pattern.