Criterion for the Selection of a Working Correlation Structure in the Generalized Estimating Equation Approach for Longitudinal Balanced Data

The generalized estimating equation is a popular method for analyzing correlated response data. It is important to determine a proper working correlation matrix at the time of applying the generalized estimating equation since an improper selection sometimes results in inefficient parameter estimates. We propose a criterion for the selection of an appropriate working correlation structure. The proposed criterion is based on a statistic to test the hypothesis that the covariance matrix equals a given matrix, and also measures the discrepancy between the covariance matrix estimator and the specified working covariance matrix. We evaluated the performance of the proposed criterion through simulation studies assuming that for each subject, the number of observations remains the same. The results revealed that when the proposed criterion was adopted, the proportion of selecting a true correlation structure was generally higher than that when other competing approaches were adopted. The proposed criterion was applied to longitudinal wheeze data, and it was suggested that the resultant correlation structure was the most accurate.

[1]  You-Gan Wang,et al.  Working‐correlation‐structure identification in generalized estimating equations , 2009, Statistics in medicine.

[2]  Edward C. Chao,et al.  Generalized Estimating Equations , 2003, Technometrics.

[3]  Myunghee C. Paik,et al.  The generalized estimating equation approach when data are not missing completely at random , 1997 .

[4]  V. Carey,et al.  Criteria for Working–Correlation–Structure Selection in GEE , 2007 .

[5]  Guoqi Qian,et al.  Selection of Working Correlation Structure and Best Model in GEE Analyses of Longitudinal Data , 2007, Commun. Stat. Simul. Comput..

[6]  Martin Crowder,et al.  On the use of a working correlation matrix in using generalised linear models for repeated measures , 1995 .

[7]  H. Nagao,et al.  On Some Test Criteria for Covariance Matrix , 1973 .

[8]  M Palta,et al.  Effect of omitted confounders on the analysis of correlated binary data. , 1997, Biometrics.

[9]  S. Zeger,et al.  Longitudinal data analysis using generalized linear models , 1986 .

[10]  B C Sutradhar,et al.  On the Accuracy of Efficiency of Estimating Equation Approach , 2000, Biometrics.

[11]  N. Jewell,et al.  Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data , 1990 .

[12]  V. Carey,et al.  Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance , 2003 .

[13]  B. Leroux,et al.  Efficiency of regression estimates for clustered data. , 1996, Biometrics.

[14]  S. Sarna,et al.  [Regression models]. , 1988, Duodecim; laaketieteellinen aikakauskirja.

[15]  N. Rao Chaganty,et al.  Analysis of Serially Correlated Data Using Quasi-Least Squares , 1998 .

[16]  G. Fitzmaurice,et al.  A caveat concerning independence estimating equations with multivariate binary data. , 1995, Biometrics.

[17]  A. Rotnitzky,et al.  A note on the bias of estimators with missing data. , 1994, Biometrics.

[18]  Kalyan Das,et al.  Miscellanea. On the efficiency of regression estimators in generalised linear models for longitudinal data , 1999 .

[19]  Geert Molenberghs,et al.  Regression Models for Longitudinal Binary Responses with Informative Drop‐Outs , 1995 .

[20]  W. Pan.  Akaike's Information Criterion in Generalized Estimating Equations , 2001, Biometrics.

[21]  Xin Tu,et al.  A comparison of several approaches for choosing between working correlation structures in generalized estimating equation analysis of longitudinal binary data , 2009, Statistics in medicine.