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

Kuo Chin Lin, Yi Ju Chen, Yu Shyr

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)4428-4439
Number of pages12
JournalStatistics in Medicine
Volume27
Issue number22
DOIs
Publication statusPublished - 2008 Sep 30

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability

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