A simple approach to analyzing clustered longitudinal data

When modeling correlated binary data in the presence of informative cluster sizes, generalized estimating equations with either resampling or inverse-weighting, are often used to correct for estimation bias. However, existing methods for the clustered longitudinal setting assume constant cluster siz...

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Bibliographic Details
Published in:Communications in statistics. Simulation and computation 2017-05, Vol.46 (5), p.3553-3562
Main Authors: Stephenson, Matthew, Ali, R. Ayesha, Darlington, Gerarda A.
Format: Article
Language:English
Subjects:
Bias
Binary data
Cluster weighting
Clustered binary data
Clusters
Computer simulation
Estimation
Generalized estimating equations (GEE)
Informative cluster size
Mathematical models
Parameter estimation
Primary 62J02 General nonlinear regression
Quantum theory
Resampling
Secondary 62J12 Generalized linear models
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Summary:When modeling correlated binary data in the presence of informative cluster sizes, generalized estimating equations with either resampling or inverse-weighting, are often used to correct for estimation bias. However, existing methods for the clustered longitudinal setting assume constant cluster sizes over time. We present a subject-weighted generalized estimating equations scheme that provides valid parameter estimation for the clustered longitudinal setting while allowing cluster sizes to change over time. We compare, via simulation, the performance of existing methods to our subject-weighted approach. The subject-weighted approach was the only method that showed negligible bias, with excellent coverage, for all model parameters.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2015.1096380