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.
Keywords
- Bias-correction
- Efficiency
- Empirical covariance matrix
- Longitudinal data
ASJC Scopus subject areas
- Statistics and Probability
- General Mathematics
- Statistics, Probability and Uncertainty