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A nonparametric smoothing method for assessing GEE models with longitudinal binary data
Author(s) -
Lin KuoChin,
Chen YiJu,
Shyr Yu
Publication year - 2008
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3315
Subject(s) - generalized estimating equation , goodness of fit , test statistic , statistics , statistic , mathematics , nonparametric statistics , gee , smoothing , binary data , binary number , statistical hypothesis testing , econometrics , arithmetic
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. Copyright © 2008 John Wiley & Sons, Ltd.