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Selection of working correlation structure in generalized estimating equations
Author(s) -
Wang YouGan,
Fu Liya
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7262
Subject(s) - selection (genetic algorithm) , generalized estimating equation , correlation , statistics , estimating equations , mathematics , computer science , econometrics , maximum likelihood , artificial intelligence , geometry
The well‐known generalized estimating equations is a very popular approach for analyzing longitudinal data. Selecting an appropriate correlation structure in the generalized estimating equations framework is a key step for estimating parameters efficiently and deriving reliable statistical inferences. We present two new criteria for selecting the best among the candidates with any arbitrary structures, even for irregularly timed measurements. The simulation results demonstrate that the new criteria perform more similarly to EAIC and EBIC as the sample size becomes large. However, their performance is much enhanced when the sample size is small and the number of measurements is large. Finally, three real datasets are used to illustrate the proposed criteria. Copyright © 2017 John Wiley & Sons, Ltd.