The analysis of very small samples of Gaussian repeated measurements can be challenging. First, due to a very small number of independent subjects contributing outcomes over time, statistical power can be quite small. Second, nuisance covariance parameters must be appropriately accounted for in the analysis in order to maintain the nominal test size. However, available statistical strategies that ensure valid statistical inference may lack power, whereas more powerful methods may have the potential for inflated test sizes. Therefore, we explore an alternative approach to the analysis of very small samples of Gaussian repeated measurements, with the goal of maintaining valid inference while also improving statistical power relative to other valid methods. This approach uses generalized estimating equations with a bias-corrected empirical covariance matrix that accounts for all small-sample aspects of nuisance correlation parameter estimation in order to maintain valid inference. Furthermore, the approach utilizes correlation selection strategies with the goal of choosing the working structure that will result in the greatest power. In our study, we show that when accurate modeling of the nuisance correlation structure impacts the efficiency of regression parameter estimation, this method can improve power relative to existing methods that yield valid inference.
|Number of pages||13|
|Journal||Statistics in Medicine|
|State||Published - Mar 15 2017|
Bibliographical noteFunding Information:
We thank an anonymous associate editor and two reviewers for their constructive comments that resulted in the improvement of this manuscript. We also thank Dr. Richard J. Kryscio, Dr. Frederick A. Schmitt, and Dr. Erin Abner for allowing us to use data from the PREADViSE trial, which was supported by a grant from the National Institute on Aging (R01 AG019241). 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 official views of the NIH.
Copyright © 2017 John Wiley & Sons, Ltd.
- correlation selection
- generalized estimating equations
- multivariate Gaussian linear model
- test size
ASJC Scopus subject areas
- Statistics and Probability