Depth importance in precision medicine (DIPM): a tree- and forest-based method for right-censored survival outcomes
Depth importance in precision medicine (DIPM): a tree- and forest-based method for right-censored survival outcomes
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DOI：
10.1093/biostatistics/kxaa021
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发表时间：
2022-01-01
期刊：
影响因子：
2.1
通讯作者：
Zhang, Heping
中科院分区：
文献类型：
--
作者：
Chen, Victoria;Zhang, Heping
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。这种比较通常是在接受一种干预措施的整个群体与其他干预措施的基础上进行的。为了识别优先待遇可能与整体群体不同的亚群体，我们在精准医学框架内针对此类数据提出了精准医学深度重要性（DIPM）方法。该方法首先修改传统分类树的分割标准以适应精准医学设置。然后，在每个节点构建随机树森林。森林用于计算每个候选分割变量的深度变量重要性得分。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构造简单的度量，它利用了更重要的变量往往被选择为更靠近树的根节点的观察结果。 DIPM 方法主要设计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的情况的扩展。我们使用模拟研究来证明我们的方法的准确性，并提供两个实际数据集的应用结果。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。总而言之，DIPM 方法产生了有希望的结果，证明了其在右删失生存结果的情况下指导个性化治疗决策的能力。
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.