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Residuals analysis of the generalized linear models for longitudinal data
Author(s) -
Chang YueCune
Publication year - 2000
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(20000530)19:10<1277::aid-sim494>3.0.co;2-s
Subject(s) - generalized estimating equation , generalized linear model , parametric statistics , statistics , mathematics , gee , wald test , generalized linear mixed model , parametric model , longitudinal data , econometrics , computer science , linear model , estimating equations , maximum likelihood , statistical hypothesis testing , data mining
The generalized estimation equation (GEE) method, one of the generalized linear models for longitudinal data, has been used widely in medical research. However, the related sensitivity analysis problem has not been explored intensively. One of the possible reasons for this was due to the correlated structure within the same subject. We showed that the conventional residuals plots for model diagnosis in longitudinal data could mislead a researcher into trusting the fitted model. A non‐parametric method, named the Wald–Wolfowitz run test, was proposed to check the residuals plots both quantitatively and graphically. The rationale proposedin this paper is well illustrated with two real clinical studies in Taiwan. Copyright © 2000 John Wiley & Sons, Ltd.