In cancer studies, it is important to understand disease heterogeneity among patients so that precision medicine can particularly target high‐risk patients at the right time. Many feature variables such as demographic variables and biomarkers, combined with a patient's survival outcome, can be used to infer such latent heterogeneity. In this work, we propose a mixture model to model each patient's latent survival pattern, where the mixing probabilities for latent groups are modeled through a multinomial distribution. The Bayesian information criterion is used for selecting the number of latent groups. Furthermore, we incorporate variable selection with the adaptive lasso into inference so that only a few feature variables will be selected to characterize the latent heterogeneity. We show that our adaptive lasso estimator has oracle properties when the number of parameters diverges with the sample size. The finite sample performance is evaluated by the simulation study, and the proposed method is illustrated by two datasets.

中文翻译：

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使用受生存结果监督的许多特征变量推断潜在异质性

在癌症研究中，了解患者之间的疾病异质性非常重要，这样精准医疗才能在正确的时间特别针对高危患者。许多特征变量，如人口统计变量和生物标志物，结合患者的生存结果，可用于推断这种潜在的异质性。在这项工作中，我们提出了一个混合模型来模拟每个患者的潜在生存模式，其中潜在组的混合概率通过多项分布建模。贝叶斯信息准则用于选择潜在组的数量。此外，我们将变量选择与自适应套索结合到推理中，以便仅选择少数特征变量来表征潜在异质性。我们表明，当参数数量与样本大小不同时，我们的自适应套索估计器具有 oracle 属性。通过模拟研究评估有限样本性能，并通过两个数据集说明所提出的方法。