NDLI: A note on model diagnostics in longitudinal data analysis

Content Provider	Springer Nature Link
Author	Yang, Kung Han Chang, Yue Cune
Copyright Year	2006
Abstract	Longitudinal study has become one of the most commonly adopted designs in medical research. The generalized estimating equations (GEE) method and/or mixed effects models are employed very often in causal inferences. The related model diagnostic procedures are not yet fully formalized, and perhaps never will be. The potential causes of major problems are the high variety of the dependence within subjects and/or the number of repeated measurements. A single testing procedure, e.g., run test, is not possible to resolve all model diagnostics problems in longitudinal data analysis. Multiple quantitative indexes for model diagnostics are needed to take into account this variety. We propose eight testing procedures for randomness accompanied with some conventional and/or non-conventional plots to remedy model diagnostics in longitudinal data analysis. The proposed issue in this paper is well illustrated with four clinical studies in Taiwan.
Starting Page	571
Ending Page	587
Page Count	17
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	21
Issue Number	3-4
e-ISSN	16139658
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-11-04
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Test for randomness Model diagnostic Longitudinal data analysis Generalized estimating equations Random effects models Economic Theory Probability Theory and Stochastic Processes Probability and Statistics in Computer Science Statistics
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

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