The Joint Model of Longitudinal and Survival Data—Based on Machine Learning Methods

本文运用机器学习方法对纵向数据与生存数据建模，以机器学习方法代替纵向子模型中的线性随机效应模型；生存子模型仍运用Cox比例危险模型。与传统的建模方法做对比，此建模方法的生存子模型残差图诊断符合理论结果，纵向子模型的残差要比线性混合模型分散。 In this paper, machine learning methods for longitudinal data and survival data modeling, replace the longitudinal sub-model linear random effects model; survival sub-model still uses Cox propor-tional hazards model. Compared with the traditional method, the residuals plots of survival sub- model diagnose modeling methods in line with theoretical results and the residuals of the longi-tudinal sub models are more dispersed than the linear mixed model.