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:: Volume 25, Issue 1 (1-2013) ::
J Islam Dent Assoc Iran 2013, 25(1): 6-15 Back to browse issues page
First and Second Order Generalized Estimating Equations and Their Application in Analyzing Longitudinal Microleakage Data
Farid Zayeri * 1, Somayeh Bardineshin 2, Ali reza Akbarzadeh-Bagheban 3, Mamak Adel 4, Saeid Asgari 5
1- Assistant Professor, Proteomics Research Center, School of Paramedical Sciences, Shahid Beheshti University of Medical Sciences. Tehran, Iran , fzayeri@yahoo.com
2- MS Student, Department of Biostatistics, School of Paramedical Sciences, Shahid Beheshti University of Medical Sciences. Tehran, Iran
3- Assistant Professor, Department of Biostatistics, School of Paramedical Sciences, Shahid Beheshti University of Medical Sciences. Tehran, Iran
4- Assistant Professor, Department of Endodontics, School of Dentistry, Qazvin University of Medical Sciences. Qazvin, Iran.
5- Professor, Endodontic Research Center, School of Dentistry, Shahid Beheshti University of Medical Sciences. Tehran, Iran
Abstract:   (7580 Views)
  Background and Aim : Longitudinal data are frequently obtained in medical studies. When the main aim of a study is marginal modeling of the mean and the correlation structure is considered as a nuisance parameter, the first- order generalized estimating equations (GEE1) is usually an appropriate option. However, when the modeling of correlation structure is considered the aim of a study, the second- order generalized estimating equations (GEE2) may be the first choice for analyzing the available data. The aim of the study was to evaluate application of first- and second-order generalized estimating equations to analyze longitudinal microleakage data.

  Materials and Methods : In this study, GEE1 and GEE2 methods were used to analyze data obtained from a study of microleakage in two root- end filling materials (CEM and MTA) in two different thicknesses and two diameters at three different times of measurement (one day, one week and one month after treatment). The obtained results from these statistical approaches were compared in continuous and binary (presence of absence) microleakage data.

  Results: The results from the GEE1 and GEE2 methods showed that time of measurement, material type, diameter and thickness of filling material had significant effects on (continuous) microleakage rate. In addition, in binary microleakage data, these methods revealed that only time and material type were the significant factors. The correlations between measurements were not significant in continuous data, while they were significant in binary response microleakage data .

  Conclusion : Since the correlations between pairs of measurements were not significant in continuous microleakage data and the obtained estimates were similar in both GEE1 and GEE2 methods, so the simpler GEE1 method seems to be adequate for these data. In contrast, in binary microleakage data, significant correlations were found between measurements. Therefore, in this case the GEE2 methodology may be used to estimate the correlation structure more efficiently .


Keywords: Microleakage, Longitudinal study, GEE1, GEE2
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Type of Study: Orginal | Subject: General
1. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986 Jan;73(1):13-22.
2. Gardiner JC, Luo Z, Roman LA. Fixed effects, random effects and GEE: What are differences? Stat in Med. 2009 Jan;28(2):221-239.
3. Doboson A. An introduction to generalized linear models, 2 nd ed. Florida: Chapman & Hall/ CRC; 2002.
4. Qu A, Song P. Assessing robustnesse of generalized estimating equations and quadratic inference functions. Biometrika 2004 Nov; 91(2): 447-459.
5. Carey V, Zeger SL, Diggle P. Modeling multivariate binary data with alternating logistic regressions. Biometrika 1993 Sep;80(3):517-526.
6. Ananth CV, Kantor ML. Modeling multivariate binary responses with multiple levels of nesting based on alternating logistic regressions: An application to caries aggregation. J Dent Res. 2004 Oct;83(10):776-781.
7. Zeger SL, Liang KY. An overview of methods for the analysis of longitudinal data. Stat In Med. 1992 Oct-Nov; 11(14-15):1825-39.
8. Farhad AR, Javadi GHR. ]An in vitro compari-son of apical microleakage in two obturation techniques: Lateral condensation and one- step[. J Isfahan Dent Sch. 2006 Spring;2(1):39-45. (Persian)
9. Tabrizizadeh M, Aghajani A. ]An in vitro eval-uation of microleakage of three temporary re-storative materials used between endodontic appointments[. J Islamic Dent Assoc of IRAN. 2002 Spring; 1(40):5-14. (Persian)
10. Al-Kahtani A, Shostad S, Schifferle R, Bhambhani S. In-vitro evaluation of microleakage of an orthograde apical plug of mineral trioxide aggregate in permanent teeth with simulated immature apices. J Endod. 2005 Feb; 31(2):117-119.
11. Torabinejad M, Watson TF, Pitt Ford TR. Sealing ability of mineral trioxide aggregate when used as a root end filling material. J Endod. 1993 Dec;19(12):591-5.
12. Razmi H, Shokouhinejad N, Fekrazad R, Motahary P, Alidoust M. ]Comparison of the sealing ability of two root- end filling material (MTA and CEM Cement) following retropreparation with ultrasonic or ER, CR: YSGG laser[. J Dent Med. 2010 Winter; 4(61):144-151. (Persian).
13. Javidi M, Naghavi N, Roohani E. Assembling of fluid filtration system for quantitative evaluation of microleakage in dental materials. Iranian Endod J. 2008 Summer; 3(3):68-72.
14. Lindsey JK. Models for repeated measurements, 2nd ed. New York: Oxford University Press; 1999.
15. Fitzmaurice GM, Laird NM, Ware JH. Applied longitudinal analysis. New York: John Wiley & Sons; 2004, Chapter 13-14.
16. Catalano PJ. Bivariate modeling of clustered continuous and ordered categorical outcomes. Stat in Med. 1997 April; 16(8):883-900.
17. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika J. 1986 Jan;73(1):13-22.
18. Lipsitz SL, Laird NM, Harrington DP. Generalized estimating equations for correlated binary data: Using the odds ratio as a measure of association. Biometrika 1991Dec;78(1):153-160.
19. Prentice RL. Correlated binary regression with covariates specific to each binary observation. Biometrics J. 1988 Dec;44(4):1033-1048.
20. Wu R, Connolly D, Ngelangel C, Bosch FX, Munoz N, Cho KR. Somatic mutations of fibro-blast growth factor receptor 3 (FGFR3) are un-common in carcinomas of the uterine cervix. Oncogene. 2000 Nov;19(48):5543-6.
21. Hardin JW, Hilbe JM. Generalized estimating equations. New York: Chapman & Hall/CRC; 2002.
22. Diggle PJ, Heagerty P, Liang KY, Zeger SL. Analysis of longitudinal data, 2nd ed. New York: Oxford University Press; 2002.
23. Qu Y, Piedmonte MR, Medendorp SV. Latent variable models for clustered ordinal data. Bio-metrics J. 1995 March;51(1):268-75.
24. Williamson JM, Lipsitz SR, Kim K. Geecat, Geegor: Computer programs for the analysis of correlated categorical response data. Computer Metho Prog In Biomed. 1999 Jan;58(1):25-34.
25. Kleinbaum DG, Klein M. Logistic regression, A self- Learning Text, 3rded. NewYork: Springer; 2005.
26. Balemi A, Lee A. Comparison of GEE1 and GEE2 estimation applied to clustered logistic regression. J Stat Comput Simul. 2009 April; 79 (4):361-378.
27. Liang KY, Zeger SL, Qaqish B. Multivariate regression analyses for categorical data. J Royal Stat Soci. Series B. 1992 Jan;54(1):3-40.
28. Abedi H, Jalalzadeh M, Khoushbin E, Rahbarzadeh GH. ]In- vitro evaluation of apical microleakage using three different root- end filling materials[. J Dent (Shiraz Univ Med Sci). 2009 Fall; 3(24):249-254. (Persian)
29. Asgary S, Eghbal MJ, Parirokh M. Sealing ability of a novel endodontic cement as a root-end filling material. J Biomed Mat Res. 2008 Dec; 87 (3):706-709.
30. Zafar M, Iravani M, Eghbal MJ, Asgary S. Coronal and apical sealing ability of a new endodontic cement. Iranian Endod J. 2009 Winter; 4(1):15-19.
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Zayeri F, Bardineshin S, Akbarzadeh-Bagheban A R, Adel M, Asgari S. First and Second Order Generalized Estimating Equations and Their Application in Analyzing Longitudinal Microleakage Data. J Islam Dent Assoc Iran. 2013; 25 (1) :6-15
URL: http://jidai.ir/article-1-1317-en.html

Volume 25, Issue 1 (1-2013) Back to browse issues page
Journal of Islamic Dental Association of Iran


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