AI 中文总结
本文在带协变量的治愈模型中，通过Copula处理相依删失，纳入行政删失并允许协变量影响所有参数，实现更准确的相依性建模与更个体化的临床相关量估计。
AI 中文摘要
在生存分析中，事件发生时间变量T常受右删失影响，研究对象可能因各种原因退出研究，或在随访结束前未发生关注事件。本文区分两种删失类型：潜在相依删失时间C（可能与T存在随机关联）和独立行政删失时间A。此外，数据可能存在治愈比例，即部分个体永远不会发生目标事件。本文基于近期关于完全参数混合治愈模型的研究，该模型通过Copula处理相依删失；所提出的扩展模型纳入行政删失，允许协变量影响所有模型参数。该框架能更准确地建模生存时间与删失时间间的相依性，同时通过协变量效应提供更大灵活性，实现对治愈比例、相依结构及其他临床相关量的更个体化估计；且协变量的存在可降低识别条件的要求。
英文摘要
In survival analysis, the time-to-event variable T is frequently subject to right censoring. Individuals may withdraw from the study for various reasons, or may not experience the event of interest before the end of follow-up. In this paper, we distinguish between two types of censoring: a potentially dependent censoring time C, which may be stochastically related to T, and an independent administrative censoring time A. In addition, the data may exhibit a cure fraction, meaning that some individuals will never experience the event. We build upon a recent work about a fully parametric mixture cure model, which accounts for dependent censoring through copulas. The proposed extension incorporates administrative censoring and allows covariates to affect all model parameters. This framework enables a more accurate modelling of the dependence between survival and censoring times while providing greater flexibility through covariate effects, leading to more individualised estimation of the cure fraction, the dependence structure, and other clinically relevant quantities. Moreover, the presence of covariates allows for weaker identification conditions.
Comments26 pages, 3 figures, 9 Tables
