Exploring causality mechanism in the joint analysis of longitudinal and survival data.

In many biomedical studies, disease progress is monitored by a biomarker over time, eg, repeated measures of CD4 in AIDS and hemoglobin in end-stage renal disease patients. The endpoint of interest, eg, death or diagnosis of a specific disease, is correlated with the longitudinal biomarker. In this paper, we examine and compare different models of longitudinal and survival data to investigate causal mechanisms, specifically, those related to the role of random effects. We illustrate the methods by data from two clinical trials: an AIDS study and a liver cirrhosis study.