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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00484851" target="_blank" >RIV/67985807:_____/18:00484851 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14310/18:00105591

Výsledek na webu

<a href="http://dx.doi.org/10.1111/eth.12713" target="_blank" >http://dx.doi.org/10.1111/eth.12713</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/eth.12713" target="_blank" >10.1111/eth.12713</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Ethology

ISSN

0179-1613

e-ISSN

—

Svazek periodika

124

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

8

Strana od-do

86-93

Kód UT WoS článku

000419978200002

EID výsledku v databázi Scopus

2-s2.0-85040669856