A nonparametric smoothing method for assessing GEE models with longitudinal binary data.
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.
Lin
Kuo-Chin
KC
Graduate Institute of Business and Management, Tainan University of Technology, Yongkang, Tainan 71002, Taiwan.
Chen
Yi-Ju
YJ
Shyr
Yu
Y
D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D008137:Longitudinal Studies; D008175:Lung Neoplasms / Q000175:diagnosis; D013058:Mass Spectrometry / Q000379:methods; D015233:Models, Statistical; D015999:Multivariate Analysis; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D018709:Statistics, Nonparametric