Missing data is a common occurrence in clinical research. Missing data occurs when the
value of the variables of interest are not measured or recorded for all subjects in the sample …

Objective Missing data is a ubiquitous problem in studies using patient-reported measures,
decreasing sample sizes and causing possible bias. In longitudinal studies, special …

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 …

Background Multiple imputation is frequently used to deal with missing data in healthcare
research. Although it is known that the outcome should be included in the imputation model …

Background: Multiple Imputation (MI) is known as an effective method for handling missing
data in public health research. However, it is not clear that the method will be effective when …

Missing data are a pervasive problem in health investigations. We describe some
background of missing data analysis and criticize ad hoc methods that are prone to serious …

Background Missing data are common in medical research, which can lead to a loss in
statistical power and potentially biased results if not handled appropriately. Multiple …

Missing data are a common challenge encountered in research which can compromise the
results of statistical inference when not handled appropriately. This paper aims to introduce …

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 …

Most epidemiologic studies will encounter missing covariate data. Software packages
typically used for analyzing data delete any cases with a missing covariate to perform a …