In epidemiological research, patient reported outcomes are often measured by a multi-item
questionnaire. In a multi-item questionnaire a construct is measured by combining the …

Missing data are frequently encountered in practice. A broader class of missing data is
called incomplete data, which includes data with measurement error, multilevel data with …

Multiple imputation (MI) is used to handle missing at random (MAR) data. Despite warnings
from statisticians, continuous variables are often recoded into binary variables. With MI it is …

Multiple imputation is a flexible technique for handling missing data that is widely used in
medical research. Its properties are understood well for some simple settings but less so for …

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 …

Missing data are a prevalent problem in psychological research that can reduce statistical
power and bias parameter estimates. These problems can be mostly resolved with multiple …

With the advent of general purpose packages that support multiple imputation for analyzing
datasets with missing data (eg, Solas, SAS PROC MI, and S-Plus 6.0), we expect much …

Missing values present challenges in the analysis of data across many areas of research.
Handling incomplete data incorrectly can lead to bias, over-confident intervals, and …

Multiple imputation methods properly account for the uncertainty of missing data. One of
those methods for creating multiple imputations is predictive mean matching (PMM), a …