Abstract:
Analysis of medical data mostly consider survival
and mortality count as response variables in identifying
factors that are associated with the survival times and with
the death count of patients. However, it is quite possible and
the literature has shown evidence for these two responses to
be correlated and share common factors. Therefore, joint
modeling of these two responses simultaneously in one model
can provide improved results than fitting two univariate
models since the correlation between the two responses can be
captured in a joint model. The literature did not consist of any
such situation where joint modelling of survival time and the
death count was considered. This manifested the objective of
developing a method for jointly modeling survival and count
responses for which a bivariate Poisson model was proposed.
This method was facilitated by the equivalence of the loglikelihoods
of survival and Poisson models. The suggested
method was fitted for a data set of Dengue patients where
factors associated with survival times of dengue patients and
death count of patients were identified by the joint model.
For comparing the performance of the proposed joint model
with two univariate models that can be fitted separately for
the two responses, the Akaike Information Criterion (AIC)
was used. It was confirmed that the performance of the joint
model surpasses the fit of two univariate models since the AIC
of the joint model was lower than the total of the AICs of the
two univariate models.
