Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data

In longitudinal studies, clinicians usually collect biomarkers’ measurements over time until an event such as recovery, disease relapse, or death occurs. Joint modeling approaches are increasingly used to study the association between one and survival outcome. However, in practice, a patient may experience multiple progression events successively. So instead of single event, multistate process should be modeled. On other hand, multivariate outcomes collected their with is interest. present study, we applied joint model various biomarkers transitions different health statuses patients who underwent renal transplantation. The full likelihood faced complexities computation likelihood. So, here, have proposed two-stage conditions avoid these complexities. showed reliable results compared case univariate biomarker process.