Methods Inf Med - Generalized estimating equations. Notes on the choice of the working correlation matrix.

Tópicos

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Resumo

CKGROUND: Generalized estimating equations (GEE) are an extension of generalized linear models (GLM) in that they allow adjusting for correlations between observations. A major strength of GEE is that they do not require the correct specification of the multivariate distribution but only of the mean structure.OBJECTIVES: Several concerns have been raised about the validity of GEE when applied to dichotomous dependent variables. In this contribution, we summarize the theoretical findings concerning efficiency and validity of GEE.METHODS: We introduce the GEE in a formal way, summarize general findings on the choice of the working correlation matrix, and show the existence of a dilemma for the optimal choice of the working correlation matrix for dichotomous dependent variables.RESULTS: Biological and statistical arguments for choosing a specific working correlation matrix are given. Three approaches are described for overcoming the range restriction of the correlation coefficient.CONCLUSIONS: The three approaches described in this article for overcoming the range restrictions for dichotomous dependent variables in GEE models provide a simple and practical way for use in applications.

Resumo Limpo

ckground general estim equat gee extens general linear model glm allow adjust correl observ major strength gee requir correct specif multivari distribut mean structureobject sever concern rais valid gee appli dichotom depend variabl contribut summar theoret find concern effici valid geemethod introduc gee formal way summar general find choic work correl matrix show exist dilemma optim choic work correl matrix dichotom depend variablesresult biolog statist argument choos specif work correl matrix given three approach describ overcom rang restrict correl coefficientconclus three approach describ articl overcom rang restrict dichotom depend variabl gee model provid simpl practic way use applic

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