REALCOM-IMPUTESoftware for Multilevel Multiple Imputation with Mixed Response Types

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

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 reflected not only in the model of interest, but also in the imputation model. In particular, the imputation model should r...

Multilevel models with multivariate mixed response types

We build upon the existing literature to formulate a class of models for multivariate mixtures of Gaussian, ordered or unordered categorical responses and continuous distributions that are not Gaussian, each of which can be defined at any level of a multilevel data hierarchy. We describe a Markov chain Monte Carlo algorithm for fitting such models. We show how this unifies a number of disparate...

Multiple imputation inference for multivariate multilevel continuous data with ignorable non-response

Methods specifically targeting missing values in a wide spectrum of statistical analyses are now part of serious statistical thinking due to many advances in computational statistics and increased awareness among sophisticated consumers of statistics. Despite many advances in both theory and applied methods for missing data, missing-data methods in multilevel applications lack equal development...

A SAS Macro for Applying Multiple Imputation to Multilevel Data

Single-level multiple imputation procedures (e.g., PROC MI) are not appropriate for multilevel data sets where observations are nested within clusters. Analyzing multilevel data imputed with a single-level procedure yields variance estimates that are biased toward zero and may yield other biased parameters. Given the prevalence of clustered data (e.g., children within schools; employees within ...

Multiple Imputation with Mplus