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

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 期刊論文 > Item 987654321/50444

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題名:	A nonparametric smoothing method for assessing GEE models with longitudinal binary data
作者:	Lin, Kuo-Chin;陳怡如;Chen, Yi-ju;Shyr, Yu
貢獻者:	淡江大學統計學系
關鍵詞:	GEE model;goodness-of-fit test;logistic regression model;longitudinal binary data;nonparametric smoothing
日期:	2008-09
上傳時間:	2010-08-09 17:28:21 (UTC+8)
出版者:	West Sussex: John Wiley & Sons Ltd.
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.
關聯:	Statistics in Medicine 27(22), pp.4428-4439
DOI:	10.1002/sim.3315
顯示於類別:	[統計學系暨研究所] 期刊論文

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