HGLM versus conditional estimators for the analysis of clustered binary data

Clustered binary data arise frequently in medical research such as cross-over clinical trials and twin studies. For the analysis of such data either a random-effects model or a conditional likelihood approach can be used. In this paper, we compare numerically the random-effects model estimator and the conditional likelihood estimator and discuss their relative merits for the analysis of binary data.

[1]  M. Patefield.  CONDITIONAL AND EXACT TESTS IN CROSSOVER TRIALS , 2000, Journal of biopharmaceutical statistics.

[2]  A R Localio,et al.  Empirical Bayes Estimation of Random Effects Parameters in Mixed Effects Logistic Regression Models , 1999, Biometrics.

[3]  G G Koch,et al.  A general methodology for the analysis of experiments with repeated measurement of categorical data. , 1977, Biometrics.

[4]  J. Kalbfleisch,et al.  The effects of mixture distribution misspecification when fitting mixed-effects logistic models , 1992 .

[5]  J. Kalbfleisch,et al.  Between- and within-cluster covariate effects in the analysis of clustered data. , 1998, Biometrics.

[6]  E. B. Andersen,et al.  Asymptotic Properties of Conditional Maximum‐Likelihood Estimators , 1970 .

[7]  Youngjo Lee.  Fixed‐effect versus random‐effect models for evaluating therapeutic preferences , 2002, Statistics in Medicine.

[8]  Youngjo Lee.  Can we recover information from concordant pairs in binary matched pairs? , 2001 .

[9]  J. Nelder,et al.  Hierarchical generalised linear models: A synthesis of generalised linear models, random-effect models and structured dispersions , 2001 .

[10]  Youngjo Lee,et al.  Hierarchical-Likelihood Approach for Mixed Linear Models with Censored Data , 2002, Lifetime data analysis.

[11]  B. G. Lindsay,et al.  Efficiency of the Conditional Score in a Mixture Setting , 1983 .

[12]  N. Breslow,et al.  Approximate inference in generalized linear mixed models , 1993 .

[13]  Youngjo Lee,et al.  Comparison of hierarchical and marginal likelihood estimators for binary outcomes , 2004, Comput. Stat. Data Anal..

[14]  J. Nelder,et al.  Hierarchical Generalized Linear Models , 1996 .

[15]  D. Cox,et al.  Parameter Orthogonality and Approximate Conditional Inference , 1987 .

[16]  J. D. Kalbfleisch,et al.  Conditions for consistent estimation in mixed-effects models for binary matched-pairs data† , 1994 .

[17]  Geert Verbeke,et al.  The Linear Mixed Model. A Critical Investigation in the Context of Longitudinal Data. , 1997 .

[18]  M. Lesperance,et al.  Estimation efficiency in a binary mixed-effects model setting , 1996 .

[19]  John A. Nelder,et al.  Conditional and Marginal Models: Another View , 2004 .

[20]  Recovery of inter-block information: extensions in a two variance component model , 1996 .

[21]  Gary G. Koch,et al.  Categorical Data Analysis Using The SAS1 System , 1995 .