Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 344-353 |
| Number of pages | 10 |
| Journal | American Statistician |
| Volume | 71 |
| Issue number | 4 |
| DOIs | |
| State | Published - Oct 2 2017 |
Bibliographical note
Publisher Copyright:© 2017 American Statistical Association.
Funding
The authors gratefully acknowledge the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1TR000117. This publication was supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1TR000117. The content is solely the responsibility of the authors and does not necessarily represent the of f icial views of the NIH. We would like to thank Dr. Richard J. Kryscio, Dr. Frederick A. Schmitt, and Dr. Erin Abner for allowing us to use the data from the PREADViSE trial, which was supported through a National Institute on Aging grant (R01 AG019241).
| Funders | Funder number |
|---|---|
| National Institutes of Health (NIH) | UL1TR000117 |
| National Institutes of Health (NIH) | |
| National Institute on Aging | R01 AG019241 |
| National Institute on Aging | |
| National Center for Research Resources | |
| National Center for Advancing Translational Sciences (NCATS) |
Keywords
- Bias-correction
- Efficiency
- Empirical covariance matrix
- Longitudinal data
ASJC Scopus subject areas
- Statistics and Probability
- General Mathematics
- Statistics, Probability and Uncertainty
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