Biostatistics and Research Decision Sciences, MRL, Merck & Co., Inc., Rahway, New Jersey, USA.
Stat Med. 2020 Dec 10;39(28):4133-4146. doi: 10.1002/sim.8714. Epub 2020 Aug 12.
In randomized clinical trials with survival outcome, there has been an increasing interest in subgroup identification based on baseline genomic, proteomic markers, or clinical characteristics. Some of the existing methods identify subgroups that benefit substantially from the experimental treatment by directly modeling outcomes or treatment effect. When the goal is to find an optimal treatment for a given patient rather than finding the right patient for a given treatment, methods under the individualized treatment regime framework estimate an individualized treatment rule that would lead to the best expected clinical outcome as measured by a value function. Connecting the concept of value function to subgroup identification, we propose a nonparametric method that searches for subgroup membership scores by maximizing a value function that directly reflects the subgroup-treatment interaction effect based on restricted mean survival time. A gradient tree boosting algorithm is proposed to search for the individual subgroup membership scores. We conduct simulation studies to evaluate the performance of the proposed method and an application to an AIDS clinical trial is performed for illustration.
在以生存结果为终点的随机临床试验中，基于基线基因组、蛋白质组标志物或临床特征的亚组识别越来越受到关注。一些现有的方法通过直接建模结局或治疗效果来识别从实验性治疗中显著获益的亚组。当目标是为特定患者找到最佳治疗方法而不是为特定治疗方法找到合适患者时，个性化治疗方案框架下的方法会估计出个体化治疗规则，该规则会根据价值函数来衡量最佳预期临床结局。将价值函数的概念与亚组识别联系起来，我们提出了一种非参数方法，通过最大化直接反映基于受限平均生存时间的亚组-治疗交互作用效果的价值函数来搜索亚组归属评分。我们提出了梯度提升树算法来搜索个体亚组归属评分。我们进行了模拟研究来评估所提出方法的性能，并对艾滋病临床试验进行了应用示例。
