A New Bayesian Model for Survival Data with a Surviving Fraction
提出了一种不同于标准混合模型的新贝叶斯模型来处理存在治愈人群的右删失生存数据，推导了比例风险结构等性质，并讨论了先验分布设定，最后用黑色素瘤临床试验数据验证。
We consider Bayesian methods for right-censored survival data for populations with a surviving (cure) fraction. We propose a model that is quite different from the standard mixture model for cure rates. We provide a natural motivation and interpretation of the model and derive several novel properties of it. First, we show that the model has a proportional hazards structure, with the covariates depending naturally on the cure rate. Second, we derive several properties of the hazard function for the proposed model and establish mathematical relationships with the mixture model for cure rates. Prior elicitation is discussed in detail, and classes of noninformative and informative prior distributions are proposed. Several theoretical properties of the proposed priors and resulting posteriors are derived, and comparisons are made to the standard mixture model. A real dataset from a melanoma clinical trial is discussed in detail.