A nonparametric smoothing method for assessing GEE models with longitudinal binary data
學年 97
學期 1
出版（發表）日期 2008-09-01
作品名稱 A nonparametric smoothing method for assessing GEE models with longitudinal binary data
作品名稱（其他語言）
著者 Lin, Kuo-Chin; 陳怡如; Chen, Yi-ju; Shyr, Yu
單位 淡江大學統計學系
出版者 West Sussex: John Wiley & Sons Ltd.
著錄名稱、卷期、頁數 Statistics in Medicine 27(22), pp.4428-4439
摘要 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.
關鍵字 GEE model; goodness-of-fit test; logistic regression model; longitudinal binary data; nonparametric smoothing
語言 en
ISSN 0277-6715
期刊性質
收錄於 SCI
產學合作
通訊作者
審稿制度
國別 GBR
公開徵稿
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