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Generalized estimating equations and regression diagnostics for longitudinal controlled clinical trials: A case study

Listed author(s):
  • Vens, Maren
  • Ziegler, Andreas
Registered author(s):

    Generalized estimating equations (GEE) were proposed for the analysis of correlated data. They are popular because regression parameters can be consistently estimated even if only the mean structure is correctly specified. GEE have been extended in several ways, including regression diagnostics for outlier detection. However, GEE have rarely been used for analyzing controlled clinical trials. The SB-LOT trial, a double-blind placebo-controlled randomized multicenter trial in which the oedema-protective effect of a vasoactive drug was investigated in patients suffering from chronic insufficiency was re-analyzed using the GEE approach. It is demonstrated that the autoregressive working correlation structure is the most plausible working correlation structure in this study. The effect of the vasoactive drug is a difference in lower leg volume of 2.64 ml per week (p=0.0288, 95% confidence interval 0.27–4.99 ml per week), making a difference of 30 ml at the end of the study. Deletion diagnostics are used for identification of outliers and influential probands. After exclusion of the most influential patients from the analysis, the overall conclusion of the study is not altered. At the same time, the goodness of fit as assessed by half-normal plots increases substantially. In summary, the use of GEE in a longitudinal clinical trial is an alternative to the standard analysis which usually involves only the last follow-up. Both the GEE and the regression diagnostic techniques should accompany the GEE analysis to serve as sensitivity analysis.

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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 56 (2012)
    Issue (Month): 5 ()
    Pages: 1232-1242

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    Handle: RePEc:eee:csdana:v:56:y:2012:i:5:p:1232-1242
    DOI: 10.1016/j.csda.2011.04.010
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    1. Oh, Sohee & Carriere, K.C. & Park, Taesung, 2008. "Model diagnostic plots for repeated measures data using the generalized estimating equations approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 222-232, September.
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    5. You-Gan Wang, 2003. "Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance," Biometrika, Biometrika Trust, vol. 90(1), pages 29-41, March.
    6. Venezuela, Maria Kelly & Sandoval, Mônica Carneiro & Botter, Denise Aparecida, 2011. "Local influence in estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1867-1883, April.
    7. John S. Preisser & Bahjat F. Qaqish & Jamie Perin, 2008. "A note on deletion diagnostics for estimating equations," Biometrika, Biometrika Trust, vol. 95(2), pages 509-513.
    8. Hammill, Bradley G. & Preisser, John S., 2006. "A SAS/IML software program for GEE and regression diagnostics," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1197-1212, November.
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