Clinical studies where patients are routinely screened for many genomic features are becoming more routine. In principle, this holds the promise of being able to find genomic signatures for a particular disease. In particular, cancer survival is thought to be closely linked to the genomic constitution of the tumor. Discovering such signatures will be useful in the diagnosis of the patient, may be used for treatment decisions and, perhaps, even the development of new treatments. However, genomic data are typically noisy and high-dimensional, not rarely outstripping the number of patients included in the study. Regularized survival models have been proposed to deal with such scenarios. These methods typically induce sparsity by means of a coincidental match of the geometry of the convex likelihood and a (near) non-convex regularizer. The disadvantages of such methods are that they are typically non-invariant to scale changes of the covariates, they struggle with highly correlated covariates, and they have a practical problem of determining the amount of regularization. In this article, we propose an extension of the differential geometric least angle regression method for sparse inference in relative risk regression models. A software implementation of our method is available on github (https://github.com/LuigiAugugliaro/dgcox).

中文翻译：

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稀疏相对风险回归模型。

定期对患者的许多基因组特征进行筛查的临床研究正变得越来越常规。原则上，这有望找到特定疾病的基因组特征。特别是，癌症存活被认为与肿瘤的基因组构成密切相关。发现这些特征将有助于患者的诊断，可用于治疗决策，甚至可能用于开发新疗法。然而，基因组数据通常是嘈杂且高维的，常常超过研究中纳入的患者数量。正则化生存模型已经被提出来处理这种情况。这些方法通常通过凸似然的几何形状与（近）非凸正则化器的巧合来引起稀疏性。此类方法的缺点是它们通常对于协变量的尺度变化是非不变的，它们与高度相关的协变量作斗争，并且它们在确定正则化量方面存在实际问题。在本文中，我们提出了微分几何最小角回归方法的扩展，用于相对风险回归模型中的稀疏推理。我们的方法的软件实现可以在 github (https://github.com/LuigiAugugliaro/dgcox) 上找到。