Missing data are frequently encountered in biomedical, epidemiologic and social research. It
is well known that a naive analysis without adequate handling of missing data may lead to …

Multiple imputation (MI) has been widely used for handling missing data in biomedical
research. In the presence of high-dimensional data, regularized regression has been used …

Missing data are common in medical and social science studies and often pose a serious
challenge in data analysis. Multiple imputation methods are popular and natural tools for …

Background Multiple imputation is frequently used to address missing data when conducting
statistical analyses. There is a paucity of research into the performance of multiple …

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 of Missing Data in Practice: Basic Theory and Analysis Strategies
provides a comprehensive introduction to the multiple imputation approach to missing data …

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

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

Missing values are a major problem in medical research. As the complete case analysis
discards useful information, estimation and inference may suffer strongly. Multiple imputation …

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 …