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Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences

SYSNO ASEP	0484851
Document Type	J - Journal Article
R&D Document Type	Journal Article
Subsidiary J	Článek ve WOS
Title	Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences
Author(s)	Pekár, S. (CZ) Brabec, Marek (UIVT-O)_{RID, SAI, ORCID}
Source Title	Ethology. - : Wiley - ISSN 0179-1613 Roč. 124, č. 2 (2018), s. 86-93
Number of pages	8 s.
Language	eng - English
Country	DE - Germany
Keywords	correlated data ; generalized estimating equations ; marginal model ; regression models ; statistical analysis
Subject RIV	BB - Applied Statistics, Operational Research
OECD category	Statistics and probability
Institutional support	UIVT-O - RVO:67985807
UT WOS	000419978200002
EID SCOPUS	85040669856
DOI	10.1111/eth.12713
Annotation	Within behavioural research, non-normally distributed data with a complicated structure are common. For instance, data can represent repeated observations of quantities on the same individual. The regression analysis of such data is complicated both by the interdependency of the observations (response variables) and by their non-normal distribution. Over the last decade, such data have been more and more frequently analysed using generalized mixed-effect models. Some researchers invoke the heavy machinery of mixed-effect modelling to obtain the desired population-level (marginal) inference, which can be achieved by using simpler tools - namely by marginal models. This paper highlights marginal modelling (using generalized estimating equations [GEE]) as an alternative method. In various situations, GEE can be based on fewer assumptions and directly generate estimates (population-level parameters) which are of immediate interest to the behavioural researcher (such as population means). Using four examples from behavioural research, we demonstrate the use, advantages, and limits of the GEE approach as implemented within the functions of the ‘geepack’ package in R.
Workplace	Institute of Computer Science
Contact	Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
Year of Publishing	2019

Number of the records: 1