Journal
AMERICAN STATISTICIAN
Volume 71, Issue 4, Pages 344-353Publisher
AMER STATISTICAL ASSOC
DOI: 10.1080/00031305.2016.1200490
Keywords
Bias-correction; Efficiency; Empirical covariance matrix; Longitudinal data
Categories
Funding
- National Center for Research Resources, National Institutes of Health [UL1TR000117]
- National Center for Advancing Translational Sciences, National Institutes of Health [UL1TR000117]
- National Institute on Aging grant [R01 AG019241]
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Correlated data are commonly analyzed using models constructed using population-averaged generalized estimating equations (GEEs). The specification of a population-averaged GEE model includes selection of a structure describing the correlation of repeated measures. Accurate specification of this structure can improve efficiency, whereas the finite-sample estimation of nuisance correlation parameters can inflate the variances of regression parameter estimates. Therefore, correlation structure selection criteria should penalize, or account for, correlation parameter estimation. In this article, we compare recently proposed penalties in terms of their impacts on correlation structure selection and regression parameter estimation, and give practical considerations for data analysts. Supplementary materials for this article are available online.
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