Missing data commonly occur in large epidemiologic studies. Ignoring incompleteness or
handling the data inappropriately may bias study results, reduce power and efficiency, and …

Statistical analysis in epidemiologic studies is often hindered by missing data, and multiple
imputation is increasingly being used to handle this problem. In a simulation study, the …

Background Multiple imputation is becoming increasingly popular for handling missing data.
However, it is often implemented without adequate consideration of whether it offers any …

When missing data occur in one or more covariates in a regression model, multiple
imputation (MI) is widely advocated as an improvement over complete‐case analysis (CC) …

The goal of multiple imputation is to provide valid inferences for statistical estimates from
incomplete data. To achieve that goal, imputed values should preserve the structure in the …

Missing covariate data commonly occur in epidemiological and clinical research, and are
often dealt with using multiple imputation. Imputation of partially observed covariates is …

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 has become one of the most popular approaches for handling missing
data in statistical analyses. Part of this success is due to Rubin's simple combination rules …

It is now widely accepted that multiple imputation (MI) methods properly handle the
uncertainty of missing data over single imputation methods. Several standard statistical …

Multiple imputation (MI) is known as an effective method for handling missing data.
However, it is not clear that the method will be effective when the data contain a high …