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

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Title:	A nonparametric smoothing method for assessing GEE models with longitudinal binary data
Authors:	Lin, Kuo-Chin;陳怡如;Chen, Yi-ju;Shyr, Yu
Contributors:	淡江大學統計學系
Keywords:	GEE model;goodness-of-fit test;logistic regression model;longitudinal binary data;nonparametric smoothing
Date:	2008-09
Issue Date:	2010-08-09 17:28:21 (UTC+8)
Publisher:	West Sussex: John Wiley & Sons Ltd.
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.
Relation:	Statistics in Medicine 27(22), pp.4428-4439
DOI:	10.1002/sim.3315
Appears in Collections:	[Graduate Institute & Department of Statistics] Journal Article

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