Problem
Research questions and friction points this paper is trying to address.
censoring
survival analysis
cure fraction
copulas
covariates
Copula based dependent censoring in cure models with covariates
🤖 AI Summary
本文通过使用copula方法扩展了混合治愈模型，以处理生存分析中的依赖性删失和管理性删失问题，并允许协变量影响所有模型参数。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Copula
Dependent Censoring
Cure Models
Covariates
Administrative Censoring
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