Conditional generalized estimating equations of mean-variance-correlation for clustered data

2021 ◽  
pp. 107386
Author(s):  
Renwen Luo ◽  
Jianxin Pan
Keyword(s):  
Generalized Estimating Equations ◽  
Clustered Data ◽  
Estimating Equations ◽  
Mean Variance ◽  
Generalized Estimating

Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations

2021 ◽  
pp. 107699862110174
Author(s):  
Francis L. Huang
Keyword(s):  
Least Squares ◽  
Generalized Estimating Equations ◽  
Multilevel Models ◽  
Clustered Data ◽  
Estimating Equations ◽  
Worked Examples ◽  
Ordinary Least Squares ◽  
Binary Outcomes ◽  
Similarities And Differences ◽  
Generalized Estimating

The presence of clustered data is common in the sociobehavioral sciences. One approach that specifically deals with clustered data but has seen little use in education is the generalized estimating equations (GEEs) approach. We provide a background on GEEs, discuss why it is appropriate for the analysis of clustered data, and provide worked examples using both continuous and binary outcomes. Comparisons are made between GEEs, multilevel models, and ordinary least squares results to highlight similarities and differences between the approaches. Detailed walkthroughs are provided using both R and SPSS Version 26.

scholarly journals A Generalized Estimating Equations Approach for Modeling Spatially Clustered Data

2021 ◽  
Vol 50 (4) ◽  
pp. 36-52
Author(s):  
Nasrin Lipi ◽  
Mohammad Samsul Alam ◽  
Syed Shahadat Hossain
Keyword(s):  
Vitamin A ◽  
Spatial Data ◽  
Generalized Estimating Equations ◽  
Clustered Data ◽  
Estimating Equations ◽  
Spatial Process ◽  
Regressive Model ◽  
Auto Regressive ◽  
Auto Regressive Model ◽  
Generalized Estimating

Clustering in spatial data is very common phenomena in various fields such as disease mapping, ecology, environmental science and so on. Analysis of spatially clustered data should be different from conventional analysis of spatial data because of the nature of clusters in the data. Because it is expected that the observations of same cluster are more similar than the observations from different clusters. In this study, a method has been proposed for the analysis of spatially clustered areal data based on generalized estimating equations which were originally developed for analyzing longitudinal data. The performance of the model for known clusters is tested in terms of how well it estimates the regression parameters and how well it captures the true spatial process. These results are presented and compared with the conditional auto-regressive model which is the most frequently used spatial model. In the simulation study, the proposed generalized estimating equations approach yields better results than the popular conditional auto-regressive model from the both perspectives of parameter estimation and spatial process capturing. A real life data on the vitamin A supplement coverage among postpartum women in Bangladesh is then analyzed for demonstration of the method. The existing divisional clustering behavior of vitamin A supplement coverage in Bangladesh is identified more accurately by the proposed approach than that by the conditional auto-regressive model.

scholarly journals Efficient interaction selection for clustered data via stagewise generalized estimating equations

2020 ◽  
Vol 39 (22) ◽  
pp. 2855-2868
Author(s):  
Gregory Vaughan ◽  
Robert Aseltine ◽  
Kun Chen ◽  
Jun Yan
Keyword(s):  
Generalized Estimating Equations ◽  
Clustered Data ◽  
Estimating Equations ◽  
Selection For ◽  
Generalized Estimating
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