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Titre du document / Document title

A nonparametric smoothing method for assessing GEE models with longitudinal binary data

Auteur(s) / Author(s)

LIN Kuo-Chin ; CHEN Yi-Ju ; SHYR Yu ;

Résumé / Abstract

Studies involving longitudinal binary responses are widely applied in the health and biomedical sciences research and frequently analyzed by generalized estimating equations (GEE) method. This article proposes an alternative goodness-of-fit test based on the nonparametric smoothing approach for assessing the adequacy of GEE fitted models, which can be regarded as an extension of the goodness-of-fit test of le Cessie and van Houwelingen (Biometrics 1991; 47:1267-1282). The expectation and approximate variance of the proposed test statistic are derived. The asymptotic distribution of the proposed test statistic in terms of a scaled chi-squared distribution and the power performance of the proposed test are discussed by simulation studies. The testing procedure is demonstrated by two real data.

Revue / Journal Title

Statistics in medicine    ISSN  0277-6715 

Source / Source

2008, vol. 27, no22, pp. 4428-4439 [12 page(s) (article)]

Langue / Language

Anglais

Editeur / Publisher

Wiley, Chichester, ROYAUME-UNI  (1982) (Revue)

Mots-clés d'auteur / Author Keywords

GEE model

;

goodness-of-fit test

;

logistic regression model

;

longitudinal binary data

;

nonparametric smoothing

;

Localisation / Location

INIST-CNRS, Cote INIST : 19624, 35400019638064.0040

Nº notice refdoc (ud4) : 20614437



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