Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments | Zendy

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Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments

Author(s) -

Ming Wang

Publication year - 2014

Publication title -

advances in statistics

Language(s) - English

Resource type - Journals

eISSN - 2356-6892

pISSN - 2314-8314

DOI - 10.1155/2014/303728

Subject(s) - gee , generalized estimating equation , marginal model , inference , statistical inference , estimating equations , sample size determination , econometrics , longitudinal data , computer science , structural equation modeling , model selection , mathematics , statistics , data science , data mining , regression analysis , artificial intelligence , maximum likelihood

Generalized Estimating Equation (GEE) is a marginal model popularly applied for longitudinal/clustered data analysis in clinical trials or biomedical studies. We provide a systematic review on GEE including basic concepts as well as several recent developments due to practical challenges in real applications. The topics including the selection of “working” correlation structure, sample size and power calculation, and the issue of informative cluster size are covered because these aspects play important roles in GEE utilization and its statistical inference. A brief summary and discussion of potential research interests regarding GEE are provided in the end

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