In survival data analysis, the Cox proportional hazards (PH) model is perhaps the most widely used model to feature the dependence of survival times on covariates. While many inference methods have been developed under such a model or its variants, those models are not adequate for handling data with complex structured covariates. High-dimensional survival data often entail several features: (1) many covariates are inactive in explaining the survival information, (2) active covariates are associated in a network structure, and (3) some covariates are error-contaminated. To hand such kinds of survival data, we propose graphical PH measurement error models and develop inferential procedures for the parameters of interest. Our proposed models significantly enlarge the scope of the usual Cox PH model and have great flexibility in characterizing survival data. Theoretical results are established to justify the proposed methods. Numerical studies are conducted to assess the performance of the proposed methods.

中文翻译：

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使用图形比例风险测量误差模型分析嘈杂的生存数据

在生存数据分析中，Cox 比例风险 (PH) 模型可能是使用最广泛的模型，用于描述生存时间对协变量的依赖性。虽然在这种模型或其变体下开发了许多推理方法，但这些模型不足以处理具有复杂结构化协变量的数据。高维生存数据通常具有几个特征：（1）许多协变量在解释生存信息时是无效的，（2）活跃的协变量与网络结构相关联，以及（3）一些协变量被错误污染。为了处理此类生存数据，我们提出了图形 PH 测量误差模型，并为感兴趣的参数开发了推理程序。我们提出的模型显着扩大了通常的 Cox PH 模型的范围，并且在表征生存数据方面具有很大的灵活性。建立了理论结果来证明所提出的方法是合理的。进行数值研究以评估所提出方法的性能。