In oncology studies, it is important to understand and characterize disease heterogeneity among patients so that patients can be classified into different risk groups and one can identify high-risk patients at the right time. This information can then be used to identify a more homogeneous patient population for developing precision medicine. In this paper, we propose a mixture survival tree approach for direct risk classification. We assume that the patients can be classified into a pre-specified number of risk groups, where each group has distinct survival profile. Our proposed tree-based methods are devised to estimate latent group membership using an EM algorithm. The observed data log-likelihood function is used as the splitting criterion in recursive partitioning. The finite sample performance is evaluated by extensive simulation studies and the proposed method is illustrated by a case study in breast cancer.

在肿瘤学研究中，了解和表征患者之间的疾病异质性非常重要，以便将患者分为不同的风险组，并在正确的时间识别高风险患者。然后，该信息可用于识别更加同质的患者群体，以开发精准医疗。在本文中，我们提出了一种用于直接风险分类的混合生存树方法。我们假设患者可以分为预先指定数量的风险组，其中每个组都有不同的生存状况。我们提出的基于树的方法旨在使用 EM 算法来估计潜在组成员资格。观察到的数据对数似然函数用作递归划分中的划分标准。通过广泛的模拟研究评估有限样本的性能，并通过乳腺癌案例研究说明所提出的方法。