Related references
Note: Only part of the references are listed.Fitting Linear Mixed-Effects Models Using lme4
Douglas Bates et al.
JOURNAL OF STATISTICAL SOFTWARE (2015)
Improving upon the efficiency of complete case analysis when covariates are MNAR
Jonathan W. Bartlett et al.
BIOSTATISTICS (2014)
Are missing data adequately handled in cluster randomised trials? A systematic review and guidelines
Karla Diaz-Ordaz et al.
CLINICAL TRIALS (2014)
Dealing With Missing Outcome Data in Randomized Trials and Observational Studies
Rolf H. H. Groenwold et al.
AMERICAN JOURNAL OF EPIDEMIOLOGY (2012)
Quantifying the impact of fixed effects modeling of clusters in multiple imputation for cluster randomized trials
Rebecca R. Andridge
BIOMETRICAL JOURNAL (2011)
To GEE or Not to GEE Comparing Population Average and Mixed Models for Estimating the Associations Between Neighborhood Risk Factors and Health
Alan E. Hubbard et al.
EPIDEMIOLOGY (2010)
Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values
Ian R. White et al.
STATISTICS IN MEDICINE (2010)
Comparison of Bayesian and classical methods in the analysis of cluster randomized controlled trials with a binary outcome: The Community Hypertension Assessment Trial (CHAT)
Jinhui Ma et al.
BMC MEDICAL RESEARCH METHODOLOGY (2009)
Imputation strategies for missing continuous outcomes in cluster randomized trials
Monica Taljaard et al.
BIOMETRICAL JOURNAL (2008)
A simulation study of odds ratio estimation for binary outcomes from cluster randomized trials
Obioha C. Ukoumunne et al.
STATISTICS IN MEDICINE (2007)
Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements
AV Hernández et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2004)
Methods to reduce the impact of intraclass correlation in group-randomized trials
DM Murray et al.
EVALUATION REVIEW (2003)
Share Paper
