标题： Cox模型中缺失分类协变量的混合方法 显示英文标题

标题： Hybrid methods for missing categorical covariates in Cox model

摘要： 生存分析旨在探讨协变量与事件发生时间之间的关系。 Cox比例风险模型常用于右删失数据，但其严格不限于此类数据。 然而，协变量中存在缺失值，尤其是分类变量，可能会损害估计的有效性。 为解决这一问题，已在Cox模型框架内提出了多种处理缺失数据的经典方法，包括参数插补、非参数插补和半参数方法。 有充分的文献表明，这些方法中没有一种在所有情况下都是理想或最优的，因此选择首选方法通常复杂且具有挑战性。 为克服这些限制，我们提出了结合经典方法优势的混合方法，以提高分析的稳健性。 通过一项模拟研究，我们证明这些混合方法相比经典方法提供了更高的灵活性、更简单的实施方式和更好的稳健性。 模拟研究的结果表明，混合方法相比经典方法具有更高的灵活性、更简单的实施方式和更强的稳健性。 特别是，它们能够减少估计偏差；然而，这种改进是以降低精度为代价的，因为变异性增加了。 这一观察结果反映了在偏差和方差之间的一个众所周知的方法论权衡，这是互补插补策略结合所固有的。 显示英文摘要

摘要： Survival analysis aims to explore the relationship between covariates and the time until the occurrence of an event. The Cox proportional hazards model is commonly used for right-censored data, but it is not strictly limited to this type of data. However, the presence of missing values among the covariates, particularly categorical ones, can compromise the validity of the estimates. To address this issue, various classical methods for handling missing data have been proposed within the Cox model framework, including parametric imputation, nonparametric imputation, and semiparametric methods. It is well-documented that none of these methods is universally ideal or optimal, making the choice of the preferred method often complex and challenging. To overcome these limitations, we propose hybrid methods that combine the advantages of classical methods to enhance the robustness of the analyses. Through a simulation study, we demonstrate that these hybrid methods provide increased flexibility, simplified implementation, and improved robustness compared to classical methods. The results from the simulation study highlight that hybrid methods offer increased flexibility, simplified implementation, and greater robustness compared to classical approaches. In particular, they allow for a reduction in estimation bias; however, this improvement comes at the cost of reduced precision, due to increased variability. This observation reflects a well-known methodological trade-off between bias and variance, inherent to the combination of complementary imputation strategies.