A note on misspecification in joint modeling of correlated data with informative cluster sizes

[1]  Zhen Chen,et al.  Modeling of correlated data with informative cluster sizes: An evaluation of joint modeling and within-cluster resampling approaches , 2017, Statistical methods in medical research.

[2]  Geert Molenberghs,et al.  On random sample size, ignorability, ancillarity, completeness, separability, and degeneracy: Sequential trials, random sample sizes, and missing data , 2014, Statistical methods in medical research.

[3]  Andrew J Copas,et al.  Methods for Observed-Cluster Inference When Cluster Size Is Informative: A Review and Clarifications , 2014, Biometrics.

[4]  A structured framework for assessing sensitivity to missing data assumptions in longitudinal clinical trials , 2013, Pharmaceutical statistics.

[5]  Somnath Datta,et al.  Inference for marginal linear models for clustered longitudinal data with potentially informative cluster sizes , 2011, Statistical methods in medical research.

[6]  Zhen Chen,et al.  A joint modeling approach to data with informative cluster size: Robustness to the cluster size model , 2011, Statistics in medicine.

[7]  Charles E McCulloch,et al.  Estimation of covariate effects in generalized linear mixed models with informative cluster sizes. , 2011, Biometrika.

[8]  Geert Molenberghs,et al.  Testing for misspecification in generalized linear mixed models. , 2010, Biostatistics.

[9]  S. Albert Paul,et al.  Shared-parameter models , 2008 .

[10]  G Molenberghs,et al.  The impact of a misspecified random‐effects distribution on the estimation and the performance of inferential procedures in generalized linear mixed models , 2008, Statistics in medicine.

[11]  F. Collins,et al.  Transforming Environmental Health Protection , 2008, Science.

[12]  Geert Molenberghs,et al.  Type I and Type II Error Under Random‐Effects Misspecification in Generalized Linear Mixed Models , 2007, Biometrics.

[13]  Ralitza V Gueorguieva,et al.  Comments about Joint Modeling of Cluster Size and Binary and Continuous Subunit‐Specific Outcomes , 2005, Biometrics.

[14]  J. N. K. Rao,et al.  Mean estimating equation approach to analysing cluster-correlated data with nonignorable cluster sizes , 2005 .

[15]  James R. Schott,et al.  Matrix Analysis for Statistics , 2005 .

[16]  N. Hjort,et al.  Frequentist Model Average Estimators , 2003 .

[17]  David B Dunson,et al.  A Bayesian Approach for Joint Modeling of Cluster Size and Subunit‐Specific Outcomes , 2003, Biometrics.

[18]  E. Leifer,et al.  Multiple Outputation: Inference for Complex Clustered Data by Averaging Analyses from Independent Data , 2003, Biometrics.

[19]  Somnath Datta,et al.  Marginal Analyses of Clustered Data When Cluster Size Is Informative , 2003, Biometrics.

[20]  Pranab Kumar Sen,et al.  Within‐cluster resampling , 2001 .

[21]  Thomas R. Ten,et al.  Two-Stage Negative Binomial and Overdispersed Poisson Models for Clustered Developmental Toxicity Data With Random Cluster Size , 1998 .

[22]  K. Burnham,et al.  Model selection: An integral part of inference , 1997 .

[23]  Pranab Kumar Sen,et al.  Large Sample Methods in Statistics: An Introduction with Applications , 1993 .

[24]  J. Horowitz Bootstrap-based critical values for the information matrix test , 1994 .

[25]  H. White Maximum Likelihood Estimation of Misspecified Models , 1982 .