Multilevel multiple imputation: A review and evaluation of joint modeling and chained equations imputation.

2016 ◽  
Vol 21 (2) ◽  
pp. 222-240 ◽  
Author(s):  
Craig K. Enders ◽  
Stephen A. Mistler ◽  
Brian T. Keller
Keyword(s):  
Multiple Imputation ◽  
Joint Modeling ◽  
Multilevel Multiple Imputation

Flexible, Free Software for Multilevel Multiple Imputation: A Review of Blimp and jomo

2019 ◽  
Vol 44 (5) ◽  
pp. 625-641
Author(s):  
Timothy Hayes
Keyword(s):  
Multiple Imputation ◽  
Interaction Model ◽  
Missing At Random ◽  
Analysis Model ◽  
Imputation Model ◽  
Model Following ◽  
Software Packages ◽  
Random Intercept ◽  
Impute Data ◽  
Multilevel Multiple Imputation

Multiple imputation is a popular method for addressing data that are presumed to be missing at random. To obtain accurate results, one’s imputation model must be congenial to (appropriate for) one’s intended analysis model. This article reviews and demonstrates two recent software packages, Blimp and jomo, to multiply impute data in a manner congenial with three prototypical multilevel modeling analyses: (1) a random intercept model, (2) a random slope model, and (3) a cross-level interaction model. Following these analysis examples, I review and discuss both software packages.

scholarly journals Multiple imputation with non-additively related variables: Joint-modeling and approximations

2016 ◽  
Vol 27 (6) ◽  
pp. 1683-1694 ◽  
Author(s):  
Soeun Kim ◽  
Thomas R Belin ◽  
Catherine A Sugar
Keyword(s):  
Multiple Imputation ◽  
Normal Structure ◽  
Joint Modeling ◽  
Continuous Variable ◽  
Approximation Methods ◽  
Multivariate Normal ◽  
Imputation Methods ◽  
Wide Range ◽  
Low Coverage ◽  
Do So

This paper investigates multiple imputation methods for regression models with interacting continuous and binary predictors when continuous variable may be missing. Usual implementations for parametric multiple imputation assume a multivariate normal structure for the variables, which is not satisfied for a binary variable nor its interaction with a continuous variable. To accommodate interactions, missing covariates are multiply imputed from conditional distribution in a manner consistent with the joint model. Alternative imputation methods under multivariate normal assumptions are also considered as candidate approximations and evaluated in a simulation study. The results suggest that the joint modeling procedure performs generally well across a wide range of scenarios and so do the approximation methods that incorporate interactions in the model appropriately by stratification. It is critical to include interactions in the imputation model as failure to do so may result in low coverage and bias. We apply the joint modeling approach and approximation methods in the study of childhood trauma with gender × trauma interaction.

scholarly journals Survival analysis with time-dependent covariates subject to missing data or measurement error: Multiple Imputation for Joint Modeling (MIJM)

Biostatistics ◽  
2017 ◽  
Vol 19 (4) ◽  
pp. 479-496 ◽  
Author(s):  
Margarita Moreno-Betancur ◽  
John B Carlin ◽  
Samuel L Brilleman ◽  
Stephanie K Tanamas ◽  
Anna Peeters ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Missing Data ◽  
Measurement Error ◽  
Multiple Imputation ◽  
Joint Modeling ◽  
Time Dependent ◽  
Time Dependent Covariates

Multiple Imputation of Missing Data at Level 2: A Comparison of Fully Conditional and Joint Modeling in Multilevel Designs

2017 ◽  
Vol 43 (3) ◽  
pp. 316-353 ◽  
Author(s):  
Simon Grund ◽  
Oliver Lüdtke ◽  
Alexander Robitzsch
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
International Student ◽  
Student Assessment ◽  
Joint Modeling ◽  
Computational Procedure ◽  
International Student Assessment ◽  
Fully Conditional Specification ◽  
Using Data ◽  
Level 2

Multiple imputation (MI) can be used to address missing data at Level 2 in multilevel research. In this article, we compare joint modeling (JM) and the fully conditional specification (FCS) of MI as well as different strategies for including auxiliary variables at Level 1 using either their manifest or their latent cluster means. We show with theoretical arguments and computer simulations that (a) an FCS approach that uses latent cluster means is comparable to JM and (b) using manifest cluster means provides similar results except in relatively extreme cases with unbalanced data. We outline a computational procedure for including latent cluster means in an FCS approach using plausible values and provide an example using data from the Programme for International Student Assessment 2012 study.

Sign in / Sign up

Export Citation Format

Share Document