Diagnostics And Multiple-imputation For Joint Modeling Of Survival And Longitudinal Outcomes

Joint modelling for longitudinal and survival data has become a key tool in clinical trial analysis,which is a rapidly growing statistical direction.The majority of the joint modeling literature has focused on the development of models that capture specific aspects of the motivating case studies.however Little attention has been given to the development of diagnostics and modelassessment tools for such joint models.Missing data in the statistical information is incomplete that can not judge the exact form of the model.We proposes a multiple-imputation-based residuals to obtain complete data,and then the residual plot diagnosis.Because the multiple-imputation data is generated from the complete data model simulation,the calculated residuals inherit the complete data characteristics,and the implementation steps of this method are simple.Furthermore,the Brier Score method is used to predict the dynamic prediction error of the joint model.