Improving the correlation structure selection approach for generalized estimating equations and balanced longitudinal data

Statistics in Medicine

Volumn 33, Issue 13, 2014, Pages 2222-2237

  Westgate, Philip M ^a  

^a UNIVERSITY OF KENTUCKY COLLEGE OF PUBLIC HEALTH   (United States)

Author keywords

Correlation structure; Efficiency; Empirical covariance matrix; Generalized estimating equations; Unstructured

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; CORRELATION ANALYSIS; COVARIANCE; CRITERION VARIABLE; DATA ANALYSIS; LONGITUDINAL STUDY; MATHEMATICAL MODEL; NORMAL DISTRIBUTION; REGRESSION ANALYSIS; RELIABILITY; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL;

DATA INTERPRETATION, STATISTICAL; LONGITUDINAL STUDIES; MODELS, STATISTICAL;

EID: 84899967344     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6106     Document Type: Article

References (43)

1. Caban-Holt A, Abner E, Kryscio RJ, Crowley JJ, Schmitt FA. Age-expanded normative data for the Ruff 2&7 Selective Attention Test: evaluating cognition in older males. The Clinical Neuropsychologist 2012; 26:751-768.
2. Chandler MJ, Lacritz LH, Hynan LS, Barnard HD, Allen G, Deschner M, Weiner MF, Cullum CM. A total score for the CERAD neuropsychological battery. Neurology 2005; 65:102-106.
3. Mathews M, Abner E, Caban-Holt A, Kryscio R, Schmitt F. CERAD practice effects and attrition bias in a dementia prevention trial. International Psychogeriatrics 2013; 25:1115-1123.
4. Liang K-Y, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:13-22.
5. Wang Y-G, Carey V. Working correlation structure misspecification, estimation and covariate design: implications for generalised estimating equations performance. Biometrika 2003; 90:29-41.
6. Ziegler A, Vens M. Generalized estimating equations: notes on the choice of the working correlation matrix. Methods of Information in Medicine 2010; 49:421-425.
7. Pan W. Akaike's information criterion in generalized estimating equations. Biometrics 2001; 57:120-125.
8. Pan W, Connett JE. Selecting the working correlation structure in generalized estimating equations with application to the lung health study. Statistica Sinica 2002; 12:475-490.
9. Hin L-Y, Wang Y-G. Working-correlation-structure identification in generalized estimating equations. Statistics in Medicine 2009; 28:642-658.
10. Rotnitzky A, Jewell NP. Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data. Biometrika 1990; 77:485-497.
11. Hin L-Y, Carey VJ, Wang Y-G. Criteria for working-correlation-structure selection in GEE. The American Statistician 2007; 61:360-364.
12. Shults J, Sun W, Tu X, Kim H, Amsterdam J, Hilbe JM, Ten-Have T. A comparison of several approaches for choosing between working correlation structures in generalized estimating equation analysis of longitudinal binary data. Statistics in Medicine 2009; 28:2338-2355.
13. Carey VJ, Wang Y-G. Working covariance model selection for generalized estimating equations. Statistics in Medicine 2011; 30:3117-3124.
14. Gosho M, Hamada C, Yoshimura I. Criterion for the selection of a working correlation structure in the generalized estimating equation approach for longitudinal balanced data. Communications in Statistics - Theory and Methods 2011; 40:3839-3856.
15. Zhou QM, Song P-K, Thompson ME. Information ratio test for model misspecification in quasi-likelihood inference. Journal of the American Statistical Association 2012; 107:205-213.
16. Barnett AG, Koper N, Dobson AJ, Schmiegelow F, Manseau M. Using information criteria to select the correct variance-covariance structure for longitudinal data in ecology. Methods in Ecology and Evolution 2010; 1:15-24.
17. Pan W. On the robust variance estimator in generalised estimating equations. Biometrika 2001; 88:901-906.
18. Westgate PM. A bias correction for covariance estimators to improve inference with generalized estimating equations that use an unstructured correlation matrix. Statistics in Medicine 2013; 32:2850-2858.
19. Crowder M. On the use of a working correlation matrix in using generalised linear models for repeated measures. Biometrika 1995; 82:407-410.
20. Sutradhar BC, Das K. On the efficiency of regression estimators in generalised linear models for longitudinal data. Biometrika 1999; 86:459-465.
21. SAS Institute Inc. SAS/STAT 9.3 User's Guide. SAS Institute Inc.: Cary, NC, 2011.
22. Mancl LA, DeRouen TA. A covariance estimator for GEE with improved small-sample properties. Biometrics 2001; 57:126-134.
23. Lu B, Preisser JS, Qaqish BF, Suchindran C, Bangdiwala SI, Wolfson M. A comparison of two bias-corrected covariance estimators for generalized estimating equations. Biometrics 2007; 63:935-941.
24. Kauermann G, Carroll RJ. A note on the efficiency of sandwich covariance matrix estimation. Journal of the American Statistical Association 2001; 96:1387-1396.
25. Hardin J, Hilbe J. Generalized Estimating Equations. Chapman & Hall/CRC: Boca Raton, Florida, 2003.
26. Shults J, Chaganty NR. Analysis of serially correlated data using quasi-least squares. Biometrics 1998; 54:1622-1630.
27. Song P-K, Jiang Z, Park E, Qu A. Quadratic inference functions in marginal models for longitudinal data. Statistics in Medicine 2009; 28:3683-3696.
28. Qu A, Lindsay BG, Li B. Improving generalised estimating equations using quadratic inference functions. Biometrika 2000; 87:823-836.
29. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria. ISBN 3-900051-07-0, 2011. Available from: http://www.R-project.org.
30. Prentice RL. Correlated binary regression with covariates specific to each binary observation. Biometrics 1988; 44:1033-1048.
31. Genz A, Bretz F, Miwa T, Mi X, Leisch F, Scheipl F, Hothorn T. mvtnorm: Multivariate Normal and t Distributions, 2013. R package version 0.9-9995. Available from: http://CRAN.R-project.org/package=mvtnorm.
32. Genz A, Bretz F. Computation of Multivariate Normal and t Probabilities, Lecture Notes in Statistics, Vol.195. Springer-Verlage: Heidelberg, 2009. ISBN 978-3-642-01688-2.
33. Leisch F, Weingessel A, Hornik K. bindata: Generation of Artificial Binary Data, 2011. R package version 0.9-18. Available from: http://CRAN.R-project.org/package=bindata.
34. Leisch F, Weingessel A, Hornik K. On the generation of correlated artificial binary data, Working Paper Series, SFB "Adaptive Information Systems and Modeling in Economics and Management Science", Vienna University of Economics, 1998. http://www.wu-wien.ac.at/am.
35. Halekoh U, Højsgaard S, Yan J. The R package geepack for generalized estimating equations. Journal of Statistical Software 2006; 15:1-11.
36. Yan J, Fine J. Estimating equations for association structures. Statistics in Medicine 2004; 23:859-874.
37. Yan J. geepack: yet another package for generalized estimating equations. R-News 2002; 2/3:12-14.
38. Boekamp JR, Strauss ME, Adams N. Estimating premorbid intelligence in African-American and white elderly veterans using the American version of the national adult reading test. Journal of Clinical and Experimental Neuropsychology 1995; 17:645-653.
39. Chen J, Lazar NA. Selection of working correlation structure in generalized estimating equations via empirical likelihood. Journal of Computational and Graphical Statistics 2012; 21:18-41.
40. Qu A, Song PX-K. Assessing robustness of generalised estimating equations and quadratic inference functions. Biometrika 2004; 91:447-459.
41. Westgate PM, Braun TM. The effect of cluster size imbalance and covariates on the estimation performance of quadratic inference functions. Statistics in Medicine 2012; 31:2209-2222.
42. Westgate PM. A bias-corrected covariance estimate for improved inference with quadratic inference functions. Statistics in Medicine 2012; 31:4003-4022.
43. Westgate PM, Braun TM. An improved quadratic inference function for parameter estimation in the analysis of correlated data. Statistics in Medicine 2013; 32:3260-3273.