Abstract:
Survival time of patients with a disease and the incidence of that particular disease
(count) is frequently observed in medical studies with the data of a clustered nature. In many
cases, though, the survival times and the count can be correlated in a way that, diseases that occur
rarely could have shorter survival times or vice versa. Due to this fact, joint modelling of these
two variables will provide interesting and certainly improved results than modelling these
separately. Authors have previously proposed a methodology using Generalized Linear Mixed
Models (GLMM) by joining the Discrete Time Hazard model with the Poisson Regression model
to jointly model survival and count model. As Aritificial Neural Network (ANN) has become a
most powerful computational tool to model complex non-linear systems, it was proposed to
develop a new joint model of survival and count of Dengue patients of Sri Lanka by using that
approach. Thus, the objective of this study is to develop a model using ANN approach and
compare the results with the previously developed GLMM model. As the response variables are
continuous in nature, Generalized Regression Neural Network (GRNN) approach was adopted
to model the data. To compare the model fit, measures such as root mean square error (RMSE),
absolute mean error (AME) and correlation coefficient (R) were used. The measures indicate the
GRNN model fits the data better than the GLMM model.
