A Joint Modeling Approach for Right Censored High Dimensional Multivariate Longitudinal DataMiran A Jaffa1*, Mulugeta Gebregziabher2 and Ayad A Jaffa3,4
- *Corresponding Author:
- Miran A Jaffa
Epidemiology and Population Health Department
Faculty of Health Sciences, American University of Beirut
Beirut, Lebanon, P.O.Box 11-0236 Riad El-Solh/Beirut
Lebanon 1107 2020. Lebanon
Tel: +961-1-350000 Ext: 4603
E-mail: [email protected]
Received date: July 02, 2014; Accepted date: July 27, 2014; Published date: July 30, 2014
Citation: Jaffa MA, Gebregziabher M, Jaffa AA (2014) A Joint Modeling Approach for Right Censored High Dimensional Multivariate Longitudinal Data. J Biomet Biostat 5:203. doi: 10.4172/2155-6180.1000203
Copyright: © 2014 Jaffa MA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are are credited.
Analysis of multivariate longitudinal data becomes complicated when the outcomes are of high dimension and informative right censoring is prevailing. Here, we propose a likelihood based approach for high dimensional outcomes wherein we jointly model the censoring process along with the slopes of the multivariate outcomes in the same likelihood function. We utilized pseudo likelihood function to generate parameter estimates for the population slopes and Empirical Bayes estimates for the individual slopes. The proposed approach was applied to jointly model longitudinal measures of blood urea nitrogen, plasma creatinine, and estimated glomerular filtration rate which are key markers of kidney function in a cohort of renal transplant patients followed from kidney transplant to kidney failure. Feasibility of the proposed joint model for high dimensional multivariate outcomes was successfully demonstrated and its performance was compared to that of a pairwise bivariate model. Our simulation study results suggested that there was a significant reduction in bias and mean squared errors associated with the joint model compared to the pairwise bivariate model.