title: A nonparametric smoothing method for assessing GEE models with longitudinal binary data.
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.
authors.affiliation: Graduate Institute of Business and Management, Tainan University of Technology, Yongkang, Tainan 71002, Taiwan.
authors.forename: Kuo-Chin
authors.initials: KC
authors.lastname: Lin
authors.forename: Yi-Ju
authors.initials: YJ
authors.lastname: Chen
authors.forename: Yu
authors.initials: Y
authors.lastname: Shyr
mesh_terms: 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