Multiple imputation (MI) is an increasingly popular method for analysing incomplete
multivariate data sets. One of the most crucial assumptions of this method relates to …

Epidemiological studies often have missing data, which are commonly handled by multiple
imputation (MI). MI is valid (given correctly-specified models) if data are missing at random …

Principled techniques for incomplete data problems are increasingly part of mainstream
statistical practice. Among many proposed techniques so far, inference by multiple …

Multivariate extensions of well-known linear mixed-effects models have been increasingly
utilized in inference by multiple imputation in the analysis of multilevel incomplete data. The …

Most current implementations of multiple imputation (MI) assume that data are missing at
random (MAR), but this assumption is generally untestable. We performed analyses to test …

Multiple imputation (MI), a two-stage process whereby missing data are imputed multiple
times and the resulting estimates of the parameter (s) of interest are combined across the …

Multiple imputation (MI) is now well established as a flexible, general, method for the
analysis of data sets with missing values. Most implementations assume the missing data …

Multiple Imputation is a flexible tool to handle missing data that has been increasingly used
in recent years. One of the conditions for its validity is that the two models used for (i) …

In the presence of missing values, researchers may be interested in the rates of missing
information. The rates of missing information are (a) important for assessing how the missing …

Background Three-level data arising from repeated measures on individuals who are
clustered within larger units are common in health research studies. Missing data are …