Rubin's multiple imputation is a three-step method for handling complex missing data, or
more generally, incomplete-data problems, which arise frequently in medical studies. At the …

Missing data is endemic in much educational research. However, practices such as step-
wise regression common in the educational research literature have been shown to be …

Background Sample loss and missing data are inevitable in multivariate and longitudinal
research. Ad hoc approaches such as analysis of incomplete data or substituting the group …

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or
mean imputation, only work under highly restrictive conditions, which are often not met in …

When sample sizes are small, a useful alternative approach to multiple imputation (ML) is to
add a prior distribution for the parameters and compute the posterior distribution of the …

Multivariate imputation by chained equations (MICE) has emerged as a principled method of
dealing with missing data. Despite properties that make MICE particularly useful for large …

Multiple imputation (MI) and full information maximum likelihood (FIML) are the two most
common approaches to missing data analysis. In theory, MI and FIML are equivalent when …

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 (MI) is increasingly being used to handle missing data in epidemiologic
research. When data on both the exposure and the outcome are missing, an alternative to …

In this article we present analytic techniques for inference from a dataset in which missing
values have been replaced by predictive means derived from an imputation model. The …