Background Case-cohort studies are conducted within cohort studies, with the defining
feature that collection of exposure data is limited to a subset of the cohort, leading to a large …

Multiple imputation (MI) is increasingly being used to handle missing data in epidemiologic
research. When data on both the exposure and the outcome are missing, an alternative to …

Conventional multiple imputation (MI) replaces the missing values in a dataset by m> 1 sets
of simulated values. I explore a two-stage extension of MI in which the missing data are …

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 …

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) …

Objectives Researchers are concerned whether multiple imputation (MI) or complete case
analysis should be used when a large proportion of data are missing. We aimed to provide …

Following the seminal publications of Rubin about thirty years ago, statisticians have
become increasingly aware of the inadequacy of “complete-case” analysis of datasets with …

Following its introduction nearly 30 years ago (Rubin, 1978), multiple imputation (MI) has
become an important and influential approach for dealing with the statistical analysis of …

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 …