Joshi Neha, Fine Jason, Chu Rong, Ivanova Anastasia
a Department of Biostatistics, The University of North Carolina at Chapel Hill , Chapel Hill , NC , USA.
b Biostatistics, Agensys, Inc , Santa Monica , CA , USA.
J Biopharm Stat. 2019;29(4):685-695. doi: 10.1080/10543406.2019.1633655. Epub 2019 Jul 4.
We consider the problem of estimating a biomarker-based subgroup and testing for treatment effect in the overall population and in the subgroup after the trial. We define the best subgroup as the subgroup that maximizes the power for comparing the experimental treatment with the control. In the case of continuous outcome and a single biomarker, both a non-parametric method of estimating the subgroup and a method based on fitting a linear model with treatment by biomarker interaction to the data perform well. Several procedures for testing for treatment effect in all and in the subgroup are discussed. Cross-validation with two cohorts is used to estimate the biomarker cut-off to determine the best subgroup and to test for treatment effect. An approach that combines the tests in all patients and in the subgroup using Hochberg's method is recommended. This test performs well in the case when there is a subgroup with sizable treatment effect and in the case when the treatment is beneficial to everyone.
我们考虑在试验后估计基于生物标志物的亚组以及在总体人群和该亚组中检验治疗效果的问题。我们将最佳亚组定义为使比较实验治疗与对照的功效最大化的亚组。在连续结局和单一生物标志物的情况下，估计亚组的非参数方法以及基于将治疗与生物标志物相互作用的线性模型拟合到数据的方法都表现良好。讨论了在总体人群和亚组中检验治疗效果的几种程序。使用两个队列的交叉验证来估计生物标志物临界值，以确定最佳亚组并检验治疗效果。建议采用一种使用霍奇伯格方法将所有患者和亚组中的检验相结合的方法。当存在具有可观治疗效果的亚组时以及当治疗对每个人都有益时，该检验表现良好。
