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Most joint models of CD4 count and viral load used for population pharmacodynamic studies have been extensions of the model proposed by Perelson et al. [156].
However, most joint models developed thus far in the literature have focused on univariate time-to-event data, where right censoring of the data acts independently of the survival process under study.
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The most popular joint modelling approach uses a shared random-effects model.
Subject-specific knee joint models were most effective in compensating for STA in terms of abduction adduction, inter external rotation and antero posterior displacement.
This will include the meshing procedures for discretization of tubular joints with through-thickness and surface cracks which are frequently regarded as one of the most difficult steps in the construction of tubular joint models.
The joint models were fitted using WinBUGS.
With respect to the Anderson Darling and Cramér-von Mises statistical tests and the tail dependence assessment, Gumbel-Hougaard copula is identified as the most suitable model for joint modelling of the AMS and CD series at each station.
The Barton Bandis (BB) joint model provides the most realistic prediction for the nonlinear shear behavior of rock joints.
As expected, when the data were generated according to the undirected joint model, the joint model was most discriminating, followed by the conditional model (p = 0.042) and FET (p<0.0001; Figure 3).
Conversely, when the data were generated according to the conditional model, the conditional model was most discriminating, followed by FET (p = 0.12) and the undirected joint model (p = 0.0013; Figure 5).
A joint model is developed, focusing on assessment of joint structural parameters and most important torque disturbances, mainly related to dependence of friction on velocity, position and temperature.
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