基于变量筛选和半参数治愈率模型的高维生存数据分析

治愈率模型已广泛应用于生存数据的分析之中，尤其是对疾病，比如癌症，的治疗干预方式的评价中。近些年来，非参数和半参数统计模型成为研究的热点和难点。由于半参数统计模型更加灵活，半参数治愈率模型更加适用于复杂结构生存数据的分析。另一方面，随着生物医学高维数据的产生，满足快速有效建模需求的变量筛选方法得到迅猛的发展。然而，针对高维生存数据复杂模型的统计分析方法尚不健全，需要更深入的研究。基于此，本项目拟从以下三个方面展开研究：1）基于治愈率模型的生物医学高维数据变量筛选方法；2）针对一般形式区间删失数据的半参数变系数治愈率模型的高效估计方法；3）半参数变系数治愈率模型的模型选择与相关的假设检验。本项目旨在基于高维数据变量筛选方法和半参数治愈率模型，实现对高维生物医学生存数据复杂模型进行快速有效的统计分析。

The cure rate model has been widely applied in survival analysis, especially for the evaluation of the therapeutic interventions for diseases like cancers. In recent years, nonparametric and semi-parametric statistical models have been hot and difficult research topics. Since semi-parametric statistical models are more flexible, the semi-parametric cure rate model is appropriate for complicated structured survival data analysis. On the other hand, with the appearance of the biological medical high dimensional data, corresponding variable screening methods are in urgent need for fast and effective modeling. variable screening methods, which can meet the needs of fast and efficient modeling, has been rapid developed. However, the statistical analysis methods of complex models for high-dimensional survival data are not perfect and are needed for further research. Based on this, the project intends to conduct the study from the following three aspects: 1) high-dimensional data variable screening methods based on the cure rate model; 2) efficient estimation methods of the semi-parametric varying coefficients cure rate model particularly for the general form interval-censored data; 3) model selection and related hypothesis testing for the semi-parametric varying coefficients cure rate model. This project aims to implement fast and effective statistical analysis for high dimensional biological and medical survival data based on variable screening methods for high dimensional data and the semi-parametric cure rate model.