Missing Data Methodology: Sensitivity analysis after multiple imputation

Within epidemiological and clinical research, missing data are a common issue which are often inappropriately handled in practice. Multiple imputation (MI) is a popular tool used to `fill in' partially observed data with plausible values drawn from an appropriate imputation distribution. Software generally implements MI under the assumption that data are `missing at random' (MAR) i.e. that the mis…