Robust Covariance Estimator for Small-Sample Adjustment in the Generalized Estimating Equations: A Simulation Study
Science Journal of Applied Mathematics and Statistics
Volume 2, Issue 1, February 2014, Pages: 20-25
Received: Jan. 21, 2014; Published: Feb. 20, 2014
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Masahiko Gosho, Advanced Medical Research Center, Aichi Medical University, Aichi, Japan
Yasunori Sato, Clinical Research Center, Chiba University Hospital, Chiba, Japan
Hisao Takeuchi, Clinical Development Center, Asahikasei Pharma Corporation, Tokyo, Japan
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The robust or sandwich estimator is common to estimate the covariance matrix of the estimated regression parameter for generalized estimating equation (GEE) method to analyze longitudinal data. However, the robust estimator would underestimate the variance under a small sample size. We propose an alternative covariance estimator to the robust estimator to improve the small-sample bias in the GEE method. Our proposed estimator is a modification of the bias-corrected covariance estimator proposed by Pan (2001, Biometrika88, 901—906) for the GEE method. In a simulation study, we compared the proposed covariance estimator to the robust estimator and Pan's estimator for continuous and binominallongitudinal responses for involving 10—50 subjects. The test size of Wald-type test statistics for the proposed estimator is relatively close to the nominal level when compared with those for the robust estimator and the Pan's approach.
Bias, Binary Response, Continuous Response, Longitudinal Data, Test Size
To cite this article
Masahiko Gosho, Yasunori Sato, Hisao Takeuchi, Robust Covariance Estimator for Small-Sample Adjustment in the Generalized Estimating Equations: A Simulation Study, Science Journal of Applied Mathematics and Statistics. Vol. 2, No. 1, 2014, pp. 20-25. doi: 10.11648/j.sjams.20140201.13
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