Joint Modelling of Two Count Variables using a Shared Random Effect Model in the presence of Clusters for Complex Data

Sooriyarachchi, M.R.

dc.contributor.author	Sooriyarachchi, M.R.
dc.date.accessioned	2021-07-07T03:28:46Z
dc.date.available	2021-07-07T03:28:46Z
dc.date.issued	2021
dc.identifier.citation	Marina Roshini Sooriyarachchi (2021). Joint modeling of two count variables using a shared random effect model in the presence of clusters for complex data BIOSTATISTICS & EPIDEMIOLOGY https://doi.org/10.1080/24709360.2021.1948381	en_US
dc.identifier.uri	http://archive.cmb.ac.lk:8080/xmlui/handle/70130/5485
dc.description.abstract	In epidemiology, it is often the case that two or more correlated count response variables are encountered. Under this scenario, it is more efficient to model the data using a joint model. Besides, if one of these count variables has an excess of zeros (spike at zero) the log link cannot be used in general. The situation is more complicated when the data is grouped into clusters. A Generalized Linear Mixed Model (GLMM) is used to accommodate this cluster covariance. The objective of this research is to develop a new modeling approach that can handle this situation. The method is illustrated on a global data set of Covid 19 patients. The important conclusions are that the new model was successfully implemented both in theory and practice. A plot of the residuals indicated a well-fitting model to the data	en_US
dc.description.sponsorship	No Sponsors	en_US
dc.language.iso	en	en_US
dc.publisher	Taylor and Francis	en_US
dc.subject	Joint model; Generalized Linear Mixed Model; cluster; spike at zero; random effects; Covid 19	en_US
dc.title	Joint Modelling of Two Count Variables using a Shared Random Effect Model in the presence of Clusters for Complex Data	en_US
dc.type	Article	en_US