Introduction of joint model and its applications in medical research

In medical follow-up studies, longitudinal data and survival data are often accompanied and associated with each other, thus respective analysis of longitudinal and survival data might lead to biased results. Joint model can correct deviations, improve the efficiency of parameter estimation and provide effective inferences by simultaneously processing longitudinal and survival data. It is a popular method in medical research. Joint model has made much progress, whereas the literature about the joint model and its application is limited in China. This paper summarizes the main idea, basic framework, parameter estimation methods of random effect joint model and introduces the analysis on AIDS data set based on the R software package 'JM’ to clarify the advantages of the joint model in processing medical follow-up data and promote the use of the joint model in clinical research. Key words: Joint model; Longitudinal data; Survival data