论文标题
单调性限制下的单索引混合固化模型
Single-index mixture cure model under monotonicity constraints
论文作者
论文摘要
我们考虑在存在治愈部分的情况下生存数据，这意味着某些受试者永远不会遇到感兴趣的事件。我们假设一个由两个子模型组成的混合疗法模型：一种是为了被取消的概率（发病率），一个用于未固定受试者的存活（潜伏期）。从参数到非参数的各种方法已被用来建模协变量对发生率的影响，而逻辑模型是最常见的模型。我们为发病率提出了一个单调单索引模型，并引入了一种新的估计方法，该方法基于剖面最大似然方法和等渗回归的技术。单调单索引结构放松参数逻辑假设，同时保持回归系数的解释性。我们研究了所提出的估计器的一致性，并通过模拟研究表明，当满足单调性假设时，与无约束的单个索引/COX混合固化模型相比，其性能更好。为了说明其实际用途，我们使用新方法研究黑色素瘤癌症生存数据。
We consider survival data in the presence of a cure fraction, meaning that some subjects will never experience the event of interest. We assume a mixture cure model consisting of two sub-models: one for the probability of being uncured (incidence) and one for the survival of the uncured subjects (latency). Various approaches, ranging from parametric to nonparametric, have been used to model the effect of covariates on the incidence, with the logistic model being the most common one. We propose a monotone single-index model for the incidence and introduce a new estimation method that is based on the profile maximum likelihood approach and techniques from isotonic regression. The monotone single-index structure relaxes the parametric logistic assumption while maintaining interpretability of the regression coefficients. We investigate the consistency of the proposed estimator and show through a simulation study that, when the monotonicity assumption is satisfied, it performs better compared to the non-constrained single-index/Cox mixture cure model. To illustrate its practical use, we use the new method to study melanoma cancer survival data.
