Abstract
Joint modeling of mixed responses has become a popular research area due to its applicability in many disciplines. When there is an association between two responses, a joint model will provide improved results than modeling the responses separately. In this study, the joint modeling of survival and count variables was carried out using Generalized Linear Mixed Modeling (GLMM) and Generalized Regression Neural Network (GRNN) to compare their performances under the setting of clustered data. A joint model of survival and count variables that was developed by joining the Discrete Time Hazard Model (DTHM) and Poisson Regression model was used in this study as the GLMM model. A simulation study was carried out under three different sample sizes; n = 20, 100, and 500, and for three levels of correlations between two responses: low (r = 0.30), moderate low (r = 0.30), and high (r = 0.30). The root mean square error, absolute mean error and correlation coefficient between actual and predicted response data were calculated to compare the performances of GLMM and GRNN models. The results revealed that the GRNN has a better fit in general, but under large sample sizes and high correlations between response variables, GLMM outperformed the GRNN. Application of these methods to a data set from poultry industry further confirmed the suitability of Generalized Regression Neural Network fit for joint modeling of real-world data.
Highlights
Joint modeling of data with mixed responses has become a popular research area in recent times
The results revealed that the Generalized Regression Neural Network (GRNN) has a better fit in general, but under large sample sizes and high correlations between response variables, Generalized Linear Mixed Modeling (GLMM) outperformed the GRNN
To assess the performance of the two models, a simulation study was carried out and the details are presented
Summary
Joint modeling of data with mixed responses has become a popular research area in recent times. Many researchers have used joint modeling techniques in various disciplines (Rizopoulos, 2012; Gardiner, 2013; Elashoff et al, 2016; Hapugoda and Sooriyarachchi, 2017a; 2017b; 2018). Many approaches of joint modeling could be identified (Rizopoulos, 2012; Gardiner, 2013). Two such methods include mixed modeling approach and artificial neural network approach (Hapugoda and Sooriyarachchi, 2017b). It will be beneficial to be aware of the performances of these approaches in joint modeling, while concluding the better fit of the models to the data
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