Abstract:
Longitudinal and time to event data are frequently encountered in many medical studies. Clinicians are more interested in
how longitudinal outcomes influences the time to an event of i nterest. To study the association between longitudinal and
time to event data, joint modeling approaches were found to be the most appropriate techniques for such data. The ap-
proaches involves the choice of the distribution of the survival times which in most cases authors prefer either exponential
or Weibull distribution. However, these distributions have some shortcomings. In this paper, we propose an alternative
joint model approach under Bayesian prospective. We assumed that survival times follow a Gompertz distribution. One
of the advantages of Gompertz distribution is that its cumulative distribution function has a closed form solution and it
accommodates time varying covariates. A Bayesian approach through Gibbs sampling procedure was developed for pa-
rameter estimation and inferences. We evaluate the finite samples performance of the joint model through an extensive
simulation study and apply the model to a real dataset to determine the association between markers(tumor sizes) and time
to death among cancer patients without recurrence. Our analysis suggested that the proposed joint modeling approach
perform well in terms of parameter estimations when correlation between random intercepts and slopes is considered.
