Choosing the Right Tool: Practical Considerations for GLMM and GEE in Longitudinal Studies, with a Focus on Data Challenges

Sihombing, Pardomuan Robinson (School of Data Science, Mathematics and Informatics, IPB University, Indonesia)
Erfiani, Erfiani (School of Data Science, Mathematics and Informatics, IPB University, Indonesia)
Notodiputro, Khairil Anwar (School of Data Science, Mathematics and Informatics, IPB University, Indonesia)
Kurnia, Anang (School of Data Science, Mathematics and Informatics, IPB University, Indonesia)

Zero : Jurnal Sains, Matematika, dan Terapan
Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan

The proposed research systematically reviews the comparative issues between GLMM and GEE for longitudinal data. The review discusses the competing arguments regarding the practical strengths and weaknesses of the two arrests. Empirical evidence demonstrates that GLMM generally provides subject-specific estimates and performs better than GEE in hierarchical and individual variance. In contrast, GEE provides resilient population-level findings, which are crucial for policy. The choice of method depends on the data structure and scope of inference. GLMM is consistently better when characterizing individuals, for example, in studies where we assume random effects are drawn from a complex distribution. GEEs shine most brightly in large datasets, obtaining robust population-level estimates even when the working correlation is misspecified. Finally, the results provide hands-on recommendations for researchers from various domains who apply statistical models to longitudinal studies to select solid, context-fitting statistical models for long-term studies.