论文标题
非线性COX回归模型的可变选择通过深度学习
Variable selection for nonlinear Cox regression model via deep learning
论文作者
论文摘要
考虑了非线性COX回归模型的可变选择问题。在生存分析中，一个主要目标是确定与经历感兴趣事件的风险相关的协变量。 COX比例危害模型在研究生存时间与协变量之间的关系中广泛使用了生存分析，该模型假定协变量对危险功能具有对数线性影响。但是，在实践中可能无法满足这种线性假设。为了提取特征的代表性子集，在线性COX模型下提出了各种可变选择方法。但是，关于非线性COX模型的可变选择的文献很少。为了打破这一差距，我们将最近开发的基于深度学习的可变选择模型套管扩展到生存数据。提供了模拟以证明所提出方法的有效性和有效性。最后，我们应用了提出的方法来分析有关弥漫性大B细胞淋巴瘤的真实数据集。
Variable selection problem for the nonlinear Cox regression model is considered. In survival analysis, one main objective is to identify the covariates that are associated with the risk of experiencing the event of interest. The Cox proportional hazard model is being used extensively in survival analysis in studying the relationship between survival times and covariates, where the model assumes that the covariate has a log-linear effect on the hazard function. However, this linearity assumption may not be satisfied in practice. In order to extract a representative subset of features, various variable selection approaches have been proposed for survival data under the linear Cox model. However, there exists little literature on variable selection for the nonlinear Cox model. To break this gap, we extend the recently developed deep learning-based variable selection model LassoNet to survival data. Simulations are provided to demonstrate the validity and effectiveness of the proposed method. Finally, we apply the proposed methodology to analyze a real data set on diffuse large B-cell lymphoma.
