REALCOM-IMPUTE Software for Multilevel Multiple Imputation with Mixed Response Types

By James R. Carpenter, Harvey Goldstein, Michael G. Kenward. 2011

364 citations recorded by the source corpus.

Multiple imputation is becoming increasingly established as the leading practical approach to modelling partially observed data, under the assumption that the data are missing at random. However, many medical and social datasets are multilevel, and this structure should be reected not only in the model of interest, but also in the imputation model. In particular, the imputation model should reect the dierences between level 1 variables and level 2 variables (which are constant across level 1 units). This led us to develop the REALCOM-IMPUTE software, which we describe in this article. This software performs multilevel multiple imputation, and handles ordinal and unordered categorical data appropriately. It is freely available on-line, and may be used either as a standalone package, or in conjunction with the multilevel software MLwiN or Stata.

Subfields: Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3a9ea66204ae9c3e7040425ad5e71d7cc56bb6bf

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Citations in the corpus

runmlwin : A Program to Run the MLwiN Multilevel Modeling Software from within Stata (2013, methodology): “A range of text books provide introductory (Hox 2010), intermediate (Raudenbush and Bryk 2002; Snijders and Bosker 2012) and advanced (Goldstein 2011) treatment of these models while two recent handbooks describe the latest developments (de Leeuw and Meijer 2008; Hox and Roberts 2010).”
Principled missing data methods for researchers (2013, methodology): “In this instance, one may turn to specialized MI programs, such as, the PAN library in the S-Plus program (Schafer 2001), the REALCON-IMPUTE software (Carpenter et al. 2011), and the R package mlmmm (Yucel 2007).”
Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data* (2014, methodology): “However, more appropriate methods for dealing with missing data (Carpenter et al., 2011) are beyond the scope of this paper, so here we use listwise deletion on all cases with missing values in the predictors and outcome that we use.”
Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non‐linear terms (2014, methodology): “Goldstein et al. (2009) and Carpenter et al. (2011) have shown how such variables can also be handled by using a latent normal approach that links the different response types through an underlying multivariate normal distribution, that is thus consistent with a range of response distributions.”
missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016, methodology): “The web page http://missingdata.lshtm.ac.uk/ of the maintainers of the software REALCOMP-IMPUTE (Carpenter, Goldstein, and Kenward 2011), which provides imputation for multi-level data, also mentions a number of resources on the topic of missing values.”
Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study (2014, methodology): “This could be properly accounted for by using hierarchical models for analysis and imputation (36).”