TY - Jour A2 - Cho，Hyungjun Au - Alafchi，Behnaz Au - Mahjub，Hossein Au - Tapak，Leili Au - Roshanaei，Ghodratollah Au - Amirzargar，Mohammad Ali Py - 2021 Da - 2021/04/09 - 两级接头Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data SP - 6641602 VL - 2021 AB - In longitudinal studies, clinicians usually collect longitudinal 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 longitudinal and one survival outcome. However, in practice, a patient may experience multiple disease progression events successively. So instead of modeling of a single event, progression of the disease as a multistate process should be modeled. On the other hand, in such studies, multivariate longitudinal outcomes may be collected and their association with the survival process is of interest. In the present study, we applied a joint model of various longitudinal biomarkers and transitions between different health statuses in patients who underwent renal transplantation. The full joint likelihood approaches are faced with the complexities in computation of the likelihood. So, here, we have proposed two-stage modeling of multivariate longitudinal outcomes and multistate conditions to avoid these complexities. The proposed model showed reliable results compared to the joint model in case of joint modeling of univariate longitudinal biomarker and the multistate process. SN - 1687-952X UR - https://doi.org/10.1155/2021/6641602 DO - 10.1155/2021/6641602 JF - Journal of Probability and Statistics PB - Hindawi KW - ER -