This tech report presents the basic concepts and methods used to deal with missing data.
After explaining the missing data mechanisms and the patterns of missingness, the main …

Missing data are ubiquitous in clinical epidemiological research. Individuals with missing
data may differ from those with no missing data in terms of the outcome of interest and …

Background Missing data is a pervasive problem in longitudinal data analysis. Several
single-imputation (SI) and multiple-imputation (MI) approaches have been proposed to …

As the" father" of multiple imputation (MI), it gives me great pleasure to be able to comment
on this collection of contributions on MI. The nice review by Paul Zhang serves as an …

Multiple imputation (MI) is a method for repairing and analyzing data with missing values. MI
replaces missing values with a sample of random values drawn from an imputation model …

In health and medical sciences, multiple imputation (MI) is now becoming popular to obtain
valid inferences in the presence of missing data. However, MI of clustered data such as …

Multiple imputation and maximum likelihood estimation (via the expectation‐maximization
algorithm) are two well‐known methods readily used for analyzing data with missing values …

Multiple imputation (MI) is one of the most popular methods to deal with missing data, and its
use has been rapidly increasing in medical studies. Although MI is rather appealing in …

Missing data occur frequently in survey and longitudinal research. Incomplete data are
problematic, particularly in the presence of substantial absent information or systematic …

Multiple imputation (MI) has become popular for analyses with missing data in medical
research. The standard implementation of MI is based on the assumption of data being …