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Modelling correlated data: Multilevel models and generalized estimating equations and their use with data from
Dimitrios Vagenas1, Vasiliki Totsika2
1Institute of Health and Biomedical Innovation, Queensland University of Technology, Australia.
Research in Developmental Disabilities
|May 23, 2018
Summary
Multilevel Models (MLM) and Generalized Estimating Equations (GEE) offer flexible analysis for clustered data in intellectual and developmental disability (IDD) research. Wider adoption requires clearer reporting standards and enhanced researcher training in these statistical methods.
Area of Science:
- Statistics in developmental disabilities research
- Advanced statistical modeling for IDD research
Background:
- Limited application of Multilevel Models (MLM) and Generalized Estimating Equations (GEE) in intellectual and developmental disability (IDD) research.
- Need for advanced statistical techniques to analyze clustered data common in IDD studies.
Purpose of the Study:
- To present key features of MLMs and GEEs for analyzing clustered data.
- To review the application of MLMs and GEEs within IDD research.
- To highlight differences in assumptions, model specification, and inference between MLMs and GEEs.
Main Methods:
- Overview of MLM and GEE functionalities, assumptions, and estimators.
- Discussion of sample size and statistical power considerations for both models.
- Examination of existing literature on MLM and GEE usage in IDD research.
Main Results:
- MLMs and GEEs are suitable for longitudinal and clustered IDD data but differ in assumptions and inference.
- MLMs require precise model specification; GEEs are more robust to misspecification but complex.
- MLMs are more prevalent in IDD research, particularly for modeling developmental trajectories.
Conclusions:
- MLMs and GEEs provide flexible analysis options for IDD clustered data.
- Increased utilization hinges on journal requirements for technical detail and simulation studies on power.
- Enhanced researcher training in basic statistical studies is crucial for broader adoption.
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