This review presents a practical summary of the missing data literature, including a sketch of
missing data theory and descriptions of normal-model multiple imputation (MI) and …

This review examines the state of Bayesian thinking as Statistics in Medicine was launched
in 1982, reflecting particularly on its applicability and uses in medical research. It then looks …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

In part 1 of this series, the authors describe the importance of incomplete data in clinical
research, and provide a conceptual framework for handling incomplete data by describing …

Multiple imputation under the assumption of multivariate normality has emerged as a
frequently used model-based approach in dealing with incomplete continuous data in recent …

Aims Analysis of binary outcomes with missing data is a challenging problem in substance
abuse studies. We consider this problem in a simple two‐group design where interest …

Methods specifically targeting missing values in a wide spectrum of statistical analyses are
now part of serious statistical thinking due to many advances in computational statistics and …

Environmental epidemiology is the study of the environmental causes of disease in
populations and how these risks vary in relation to intensity and duration of exposure and …

Situations in which multiple outcomes and predictors of different distributional types are
collected are becoming increasingly common in biopharmaceutical practice, and joint …

The true missing data mechanism is never known in practice. We present a method for
generating multiple imputations for binary variables, which formally incorporates missing …