Yemane Hailu Fissuh
This paper is motivated in modeling a joint mixed effect model incorporating random effects with independent measurement error for both end points. Both the association in the evolution (AOE) for two or possibly multiple outcomes and evolution in the association (EOA) are expected to be assessed by joint mixed effect model. The proposed model is further trustful of grasping the problem of nonlinearity and absence of normality assumption and in turn is to predict the effect of associated covariates in the progressive evolution of longitudinal outcomes throughout the given time interval. As case study the two outcomes Systolic Blood Pressure (SBP) and Diastolic Blood Pleasure (DBP) of hypertensive patients are considered. The summary statistics of the two end points are included in this context. Thus, the average follow-up is 4.21(0.088) months, the average SBP and DBP of hypertensive patients are 136.12(0.367) and 85.13(0.273) respectively and the standard deviation of SBP and DBP are 16.21 and 12.06 respectively. Moreover, the average age of the hypertensive patients is 50.63(0.315) years old. The values inside the brackets refer the standard errors. Finally, the straight lines on the two plots indicate the normality of the two outcomes. This study suggests for the further work to the extended non-linear mixed effect model for correlated multivariate repeated measure data usually called longitudinal data. Moreover, the study can incorporate the joint model of multivariate longitudinal outcome with time to event outcomes. In order to come up with flexible and robust models, the authors can further extend these models to non-parametric smoothing models of longitudinal endpoints and survival times.
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