In the field of cardio-thoracic surgery, valve function is monitored over
time after surgery. The motivation for our research comes from a study which
includes patients who received a human tissue valve in the aortic position.
These patients are followed prospectively over time by standardized
echocardiographic assessment of valve function. Loss of follow-up could be
caused by valve intervention or the death of the patient. One of the main
characteristics of the human valve is that its durability is limited.
Therefore, it is of interest to obtain a prognostic model in order for the
physicians to scan trends in valve function over time and plan their next
intervention, accounting for the characteristics of the data.
Several authors have focused on deriving predictions under the standard joint
modeling of longitudinal and survival data framework that assumes a constant
effect for the coefficient that links the longitudinal and survival outcomes.
However, in our case this may be a restrictive assumption. Since the valve
degenerates, the association between the biomarker with survival may change
over time.
To improve dynamic predictions we propose a Bayesian joint model that allows
a time-varying coefficient to link the longitudinal and the survival processes,
using P-splines. We evaluate the performance of the model in terms of
discrimination and calibration, while accounting for censoring
