Han Baoguang, Yu Menggang, McEntegart Damian
Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN 46285, USA.
Pharm Stat. 2013 Jul-Aug;12(4):243-53. doi: 10.1002/pst.1577. Epub 2013 Jun 13.
Re-randomization test has been considered as a robust alternative to the traditional population model-based methods for analyzing randomized clinical trials. This is especially so when the clinical trials are randomized according to minimization, which is a popular covariate-adaptive randomization method for ensuring balance among prognostic factors. Among various re-randomization tests, fixed-entry-order re-randomization is advocated as an effective strategy when a temporal trend is suspected. Yet when the minimization is applied to trials with unequal allocation, fixed-entry-order re-randomization test is biased and thus compromised in power. We find that the bias is due to non-uniform re-allocation probabilities incurred by the re-randomization in this case. We therefore propose a weighted fixed-entry-order re-randomization test to overcome the bias. The performance of the new test was investigated in simulation studies that mimic the settings of a real clinical trial. The weighted re-randomization test was found to work well in the scenarios investigated including the presence of a strong temporal trend.
重新随机化检验被认为是一种强大的替代方法，可用于替代传统的基于总体模型的方法来分析随机临床试验。当临床试验根据最小化原则进行随机分组时尤其如此，最小化是一种流行的协变量适应性随机化方法，用于确保预后因素之间的平衡。在各种重新随机化检验中，当怀疑存在时间趋势时，固定进入顺序重新随机化被倡导为一种有效的策略。然而，当将最小化应用于分配不均的试验时，固定进入顺序重新随机化检验存在偏差，因此检验效能会受到影响。我们发现这种偏差是由于在这种情况下重新随机化产生的非均匀重新分配概率所致。因此，我们提出了一种加权固定进入顺序重新随机化检验来克服这种偏差。在模拟研究中对新检验的性能进行了调查，这些模拟研究模仿了真实临床试验的设置。结果发现，加权重新随机化检验在所研究的场景中表现良好，包括存在强烈时间趋势的情况。
