Feng Huaibao, Liu Qing
Clinical Biostatistics, Janssen Research & Development LLC, Raritan, NJ, USA.
J Biopharm Stat. 2012;22(4):662-78. doi: 10.1080/10543406.2012.678808.
Standard group sequential test assumes that the treatment effects are homogeneous over time. In practice, however, this assumption may be violated. Often, this occurs when treatment effects are heterogeneous in patients with different prognostic groups, which are not evenly distributed over the time course of the group sequential trial. In this article, we consider a setting where the inclusion/exclusion criteria for patient entry are relaxed at interim analyses. This triggers heterogeneous treatment effects over the enlarged patient population. In particular, we assume that the population change relates to some baseline covariates. Simulation results show that the type I error can be severely inflated if adjustment is not made to the statistical analysis. We consider a set of linear regression models. With these models, we make inference on the target population based on all data from the changed populations. The proposed method leads to unbiased inference.
标准序贯检验假定治疗效果随时间是同质的。然而，在实际中，这一假定可能会被违背。通常，当不同预后组的患者治疗效果存在异质性，且这些预后组在序贯试验的时间进程中分布不均时，就会出现这种情况。在本文中，我们考虑一种在期中分析时放宽患者入组的纳入/排除标准的情形。这会在扩大的患者群体中引发异质性治疗效果。特别地，我们假定群体变化与一些基线协变量有关。模拟结果表明，如果不对统计分析进行调整，第一类错误可能会严重膨胀。我们考虑一组线性回归模型。利用这些模型，我们基于来自变化群体的所有数据对目标群体进行推断。所提出的方法能得出无偏推断。
