Dealing with missing covariates in epidemiologic studies: a comparison between multiple imputation and a full Bayesian approach

Fitting Linear Mixed-Effects Models Usinglme4

(2015) Douglas Bates et al.   Journal of Statistical Software

Convergence Properties of a Sequential Regression Multiple Imputation Algorithm

(2015) Jian Zhu et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

State of the Multiple Imputation Software

(2015) Recai M. Yucel   Journal of Statistical Software

mice: Multivariate Imputation by Chained Equations inR

(2015) Stef van Buuren et al.   Journal of Statistical Software

Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model

(2014) Jonathan W Bartlett et al.   STATISTICAL METHODS IN MEDICAL RESEARCH

What Is Meant by “Missing at Random”?

(2013) Shaun Seaman et al.   STATISTICAL SCIENCE

The Generation R Study: design and cohort update 2012

(2012) Vincent W. V. Jaddoe et al.   EUROPEAN JOURNAL OF EPIDEMIOLOGY

Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates

(2011) Baojiang Chen et al.   BIOMETRICS

Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example

(2010) Mirjam J. Knol et al.   JOURNAL OF CLINICAL EPIDEMIOLOGY

Missing covariate data in medical research: To impute is better than to ignore

(2010) Kristel J.M. Janssen et al.   JOURNAL OF CLINICAL EPIDEMIOLOGY

Weighted Generalized Estimating Functions for Longitudinal Response and Covariate Data That Are Missing at Random

(2010) Baojiang Chen et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

Multiple imputation using chained equations: Issues and guidance for practice

(2010) Ian R. White et al.   STATISTICS IN MEDICINE

Multilevel models with multivariate mixed response types

(2009) Harvey Goldstein et al.   STATISTICAL MODELLING