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Joint modeling of two count variables using a shared random effect model in the presence of clusters for complex data

Joint modeling of two count variables using a shared random effect model in the presence of... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Joint modeling of two count variables using a shared random effect model in the presence of clusters for complex data

Joint modeling of two count variables using a shared random effect model in the presence of clusters for complex data

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...
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Publisher
Taylor & Francis
Copyright
© 2021 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2021.1948381
Publisher site
See Article on Publisher Site

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.

Journal

Biostatistics & EpidemiologyTaylor & Francis

Published: Jan 2, 2022

Keywords: Joint model; Generalized Linear Mixed Model; cluster; spike at zero; Covid 19

References