Department of Computer and Information Science, Linköping University, Linköping, Sweden.
Department of Women's and Children's Health, Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden.
Stat Med. 2019 Sep 30;38(22):4436-4452. doi: 10.1002/sim.8308. Epub 2019 Jun 27.
Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of personalized medicine, which aims at finding subgroups of the patients with similar characteristics for which some of the investigated treatments have a better effect than the other treatments. A number of approaches based on decision trees have been proposed to identify such subgroups, but most of them focus on two-arm trials (control/treatment) while a few methods consider quantitative treatments (defined by the dose). However, no subgroup identification method exists that can predict the best treatments in a scenario with a categorical set of treatments. We propose a novel method for subgroup identification in categorical treatment scenarios. This method outputs a decision tree showing the probabilities of a given treatment being the best for a given group of patients as well as labels showing the possible best treatments. The method is implemented in an R package psica available on CRAN. In addition to a simulation study, we present an analysis of a community-based nutrition intervention trial that justifies the validity of our method.
个性化医学旨在为具有特定特征的患者确定最佳治疗方法。文献已经表明，与传统方法将相同的治疗方法开给所有患者相比，这些方法可以极大地改善医学。亚组识别是个性化医学的一个分支，旨在找到具有相似特征的患者亚组，对于这些亚组中的一些被调查的治疗方法，其效果要好于其他治疗方法。已经提出了许多基于决策树的方法来识别这些亚组，但大多数方法都集中在两臂试验（对照/治疗）上，而少数方法则考虑了定量治疗（由剂量定义）。然而，目前还没有一种亚组识别方法可以在具有分类治疗方案的情况下预测最佳治疗方法。我们提出了一种用于分类治疗方案的亚组识别的新方法。该方法输出一棵决策树，显示给定治疗方法对于给定患者群体的最佳概率，以及显示可能的最佳治疗方法的标签。该方法已在 CRAN 上的 R 包 psica 中实现。除了模拟研究外，我们还介绍了一项基于社区的营养干预试验的分析，该分析证明了我们方法的有效性。
