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A note on model diagnostics in longitudinal data analysis

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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.

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Acknowledgements

The authors wish to thank Associate Editor and referees for their helpful comments and suggestions. We also would like to thank Dr Ko, Mei-Lan for kindly providing the diabetic retinopathy data, the GK study group in VGH-Taipei for providing the meningiomas data, and Dr. Lane, Hsien-Yuan. for providing the schizophrenia’s data.

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Authors and Affiliations

  1. Department of Mathematics, Tamkang University, Tamsui, 25137, Taiwan, ROC

    Kung Han Yang & Yue-Cune Chang

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  1. Kung Han Yang
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  2. Yue-Cune Chang
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Correspondence to Yue-Cune Chang.

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Yang, K.H., Chang, YC. A note on model diagnostics in longitudinal data analysis. Computational Statistics 21, 571–587 (2006). https://doi.org/10.1007/s00180-006-0015-y

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