A PRESS statistic for working correlation structure selection in generalized estimating equations

References

• D.A. Belsey, E. Kuh, and R.E. Welsch, Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, John Wiley & Sons, New York, 2005.
   Google Scholar
• E. Cantoni, J.M. Flemming, and E. Ronchetti, Variable selection for marginal longitudinal generalized linear models, Biometrics 61 (2005), pp. 507–514. doi: 10.1111/j.1541-0420.2005.00331.x
   PubMed Web of Science ®Google Scholar
• V.J. Carey, T. Lumley, and B. Ripley, gee: Generalized estimation equation solver, R package version 4.13–19, 2015. Available at https://cran.r-project.org/web/packages/gee/.
   Google Scholar
• V.J. Carey and Y.G. Wang, Working covariance model selection for generalized estimating equations, Stat. Med. 30 (2011), pp. 3117–3124. doi: 10.1002/sim.4300
   PubMed Web of Science ®Google Scholar
• H. Demirtas and D. Hedeker, A practical way for computing approximate lower and upper correlation bounds, Am. Stat. 65 (2011), pp. 104–109. doi: 10.1198/tast.2011.10090
   Web of Science ®Google Scholar
• M. Gosho, C. Hamada, and I. Yoshimura, Criterion for the selection of a working correlation structure in the generalized estimating equation approach for longitudinal balanced data, Comm. Statist. Theory Methods 40 (2011), pp. 3839–3856. doi: 10.1080/03610926.2010.501938
   Web of Science ®Google Scholar
• B.G. Hammill and J.S. Preisser, A SAS/IML software program for GEE and regression diagnostics, Comput. Statist. Data Anal. 51 (2006), pp. 1197–1212. doi: 10.1016/j.csda.2005.11.016
   Web of Science ®Google Scholar
• L.Y. Hin and Y.G. Wang, Working-correlation-structure identification in generalized estimating equations, Stat. Med. 28 (2009), pp. 642–658. doi: 10.1002/sim.3489
   PubMed Web of Science ®Google Scholar
• S. Hojsgaard, U. Halekoh, and J. Yan, geepack: Generalized estimating equation, R package version 1.2–0.1, 2016. Available at https://cran.r-project.org/web/packages/geepack.
   Google Scholar
• G. Inan and H. Demirtas, PoisBinNonNor: Data generation with Poisson, binary and continuous components, R package version 1.1, 2016. Available at https://cran.r-project.org/web/packages/PoisBinNonNor.
   Google Scholar
• G. Inan, M.A.H.M. Latif, and J. Preisser, A prediction criterion for working correlation structure selection in GEE, 2018. Available at http://arxiv.org/abs/1803.06383.
   Google Scholar
• G. Inan, J. Zhou, and L. Wang, PGEE: Penalized generalized estimating equations in high-dimension, R package version 1.5, 2017. Available at https://cran.r-project.org/web/packages/PGEE.
   Google Scholar
• A. Jaman, M. Latif, W. Bari, and A.S. Wahed, A determinant based criterion for working correlation structure selection in generalized estimating equations, Stat. Med. 35 (2015), pp. 1819–1833. doi: 10.1002/sim.6821
   PubMed Web of Science ®Google Scholar
• K.Y. Liang and S.L. Zeger, Longitudinal data analysis using generalized linear models, Biometrika 73 (1986), pp. 13–22. doi: 10.1093/biomet/73.1.13
   Web of Science ®Google Scholar
• L.A. Mancl and T.A. DeRouen, A covariance estimator for gee with improved small-sample properties, Biometrics 57 (2001), pp. 126–134. doi: 10.1111/j.0006-341X.2001.00126.x
   PubMed Web of Science ®Google Scholar
• W. Pan, Akaike's information criterion in generalized estimating equations, Biometrics 57 (2001), pp. 120–125. doi: 10.1111/j.0006-341X.2001.00120.x
   PubMed Web of Science ®Google Scholar
• J. Perin, J.S. Preisser, and R.J. Rathouz, Semiparametric efficient estimation for incomplete longitudinal binary data, with application to smoking trends, J. Amer. Statist. Assoc. 104 (2009), pp. 1373–1384. doi: 10.1198/jasa.2009.ap08527
   Web of Science ®Google Scholar
• J.S. Preisser, B. Lu, and B.F. Qaqish, Finite sample adjustments in estimating equations and covariance estimators for intracluster correlations, Stat. Med. 27 (2008), pp. 5764–5785. doi: 10.1002/sim.3390
   PubMed Web of Science ®Google Scholar
• J.S. Preisser and B.F. Qaqish, Deletion diagnostics for generalised estimating equations, Biometrika 83 (1996), pp. 551–562. doi: 10.1093/biomet/83.3.551
   Web of Science ®Google Scholar
• J.S. Preisser, B.F. Qaqish, and J. Perin, A note on deletion diagnostics for estimating equations, Biometrika 95 (2008), pp. 509–513. doi: 10.1093/biomet/asn019
   Web of Science ®Google Scholar
• R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2013.
   Google Scholar
• A. Rotnitzky and N.P. Jewell, Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data, Biometrika 77 (1990), pp. 485–497. doi: 10.1093/biomet/77.3.485
   Web of Science ®Google Scholar
• SAS Institute Inc., SAS/STAT 9.4 User's Guide, SAS Institute Inc., Cary, NC, 2013.
   Google Scholar
• J. Shults and N.R. Chaganty, Analysis of serially correlated data using quasi-least squares, Biometrics 54 (1998), pp. 1622–1630. doi: 10.2307/2533686
   Web of Science ®Google Scholar
• J. Shults, W. Sun, X. Tu, H. Kim, J. Amsterdam, J.M. Hilbe, and T. Ten-Have T, A comparison of several approaches for choosing between working correlation structures in generalized estimating equation analysis of longitudinal binary data, Stat. Med. 28 (2009), pp. 2338–2355. doi: 10.1002/sim.3622
   PubMed Web of Science ®Google Scholar
• Y.G. Wang and V. Carey, Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance, Biometrika 90 (2003), pp. 29–41. doi: 10.1093/biomet/90.1.29
   Web of Science ®Google Scholar
• P.M. Westgate, A bias correction for covariance estimators to improve inference with generalized estimating equations that use an unstructured correlation matrix, Stat. Med. 32 (2013), pp. 2850–2858. doi: 10.1002/sim.5709
   PubMed Web of Science ®Google Scholar
• P.M. Westgate, Criterion for the simultaneous selection of a working correlation structure and either generalized estimating equations or the quadratic inference function approach, Biom. J. 56 (2014), pp. 461–476. doi: 10.1002/bimj.201300098
   PubMed Web of Science ®Google Scholar
• P.M. Westgate, Improving the correlation structure selection approach for generalized estimating equations and balanced longitudinal data, Stat. Med. 33 (2014), pp. 2222–2237. doi: 10.1002/sim.6106
   PubMed Web of Science ®Google Scholar
• P.M. Westgate, A covariance correction that accounts for correlation estimation to improve finite-sample inference with generalized estimating equations: A study on its applicability with structured correlation matrices, J. Stat. Comput. Simul. 86 (2015), pp. 1891–1900. doi: 10.1080/00949655.2015.1089873
   PubMed Web of Science ®Google Scholar
• P.M. Westgate and W.W. Burchett, A comparison of correlation structure selection penalties for generalized estimating equations, Am. Stat. 71 (2016), pp. 344–353. doi: 10.1080/00031305.2016.1200490
   Web of Science ®Google Scholar
• Z. Xiaolu and Z. Zhongyi, Comparison of criteria to select working correlation matrix in generalized estimating equations, Chinese J. Appl. Probab 29 (2013), pp. 515–530.
   Google Scholar
• J. Xu, Q. Yang, and J. Zhang, The performances of several modified CIC criteria for working intra-cluster correlation structure selection in GEE analysis, Commun. Stat. Simul. C 46 (2017), pp. 6034–6048. doi: 10.1080/03610918.2016.1189565
   Web of Science ®Google Scholar