Many clinical trials have been conducted to compare right-censored survival outcomes between interventions. Such comparisons are typically made on the basis of the entire group receiving one intervention versus the others. In order to identify subgroups for which the preferential treatment may differ from the overall group, we propose the depth importance in precision medicine (DIPM) method for such data within the precision medicine framework. The approach first modifies the split criteria of the traditional classification tree to fit the precision medicine setting. Then, a random forest of trees is constructed at each node. The forest is used to calculate depth variable importance scores for each candidate split variable. The variable with the highest score is identified as the best variable to split the node. The importance score is a flexible and simply constructed measure that makes use of the observation that more important variables tend to be selected closer to the root nodes of trees. The DIPM method is primarily designed for the analysis of clinical data with two treatment groups. We also present the extension to the case of more than two treatment groups. We use simulation studies to demonstrate the accuracy of our method and provide the results of applications to two real-world data sets. In the case of one data set, the DIPM method outperforms an existing method, and a primary motivation of this article is the ability of the DIPM method to address the shortcomings of this existing method. Altogether, the DIPM method yields promising results that demonstrate its capacity to guide personalized treatment decisions in cases with right-censored survival outcomes.

中文翻译：

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精准医学 (DIPM) 中的深度重要性：一种基于树和森林的右删失生存结果方法。

已经进行了许多临床试验来比较干预措施之间的右删失生存结果。这种比较通常是在整个组接受一种干预与其他干预的基础上进行的。为了确定优惠待遇可能与整体组不同的亚组，我们提出了精准医学框架内此类数据的精准医学深度重要性（DIPM）方法。该方法首先修改了传统分类树的分割标准，以适应精准医学设置。然后，在每个节点处构建一个随机的树森林。森林用于计算每个候选分割变量的深度变量重要性分数。得分最高的变量被识别为分割节点的最佳变量。重要性得分是一种灵活且简单构造的度量，它利用观察到更重要的变量倾向于选择更接近树的根节点。DIPM 方法主要用于分析两个治疗组的临床数据。我们还提出了对两个以上治疗组情况的扩展。我们使用模拟研究来证明我们方法的准确性，并将应用结果提供给两个真实世界的数据集。在一个数据集的情况下，DIPM 方法优于现有方法，本文的主要动机是 DIPM 方法能够解决现有方法的缺点。共，