Analysis of Clustered Polytomous Data Using Generalized Estimating Equations and Working Covariance Structures

[1]  R. Prentice,et al.  Correlated binary regression with covariates specific to each binary observation. , 1988, Biometrics.

[2]  Andrea Rotnitzky,et al.  Regression Models for Discrete Longitudinal Responses , 1993 .

[3]  R. Hines.  Fitting generalized linear models to retrospectively sampled clusters with categorical responses , 1997 .

[4]  P. Diggle,et al.  Analysis of Longitudinal Data. , 1997 .

[5]  D L DeMets,et al.  A comparison of methods for correlated ordinal measures with ophthalmic applications. , 1995, Statistics in medicine.

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

[7]  G. Molenberghs,et al.  Marginal Modeling of Correlated Ordinal Data Using a Multivariate Plackett Distribution , 1994 .

[8]  J. R. Landis,et al.  The analysis of longitudinal polytomous data: generalized estimating equations and connections with weighted least squares. , 1993, Biometrics.

[9]  Michael I. Miller.  Analysing categorical responses obtained from large clusters , 1995 .

[10]  R. Hines,et al.  An Application of retrospective sampling in the analysis of a very large clustered data set , 1997 .

[11]  S. Lipsitz,et al.  Analysis of repeated categorical data using generalized estimating equations. , 1994, Statistics in medicine.

[12]  P. Diggle.  Analysis of Longitudinal Data , 1995 .

[13]  T. Lumley.  Generalized estimating equations for ordinal data: a note on working correlation structures. , 1996, Biometrics.

[14]  J. Neuhaus.  Estimation efficiency and tests of covariate effects with clustered binary data. , 1993, Biometrics.

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