Course summary - Multiple Imputation

Multiple imputation is becoming increasingly popular for handling missing data. This one-day course provides an introduction to multiple imputation and the practical issues faced by researchers wishing to apply this approach. 

In particular the course focuses on understanding when multiple imputation is likely to produce substantial gains over a standard complete case analysis, and on the decisions faced when developing an imputation model, once it has been decided that multiple imputation is appropriate.

We provide a detailed introduction, with practical computing exercises on how to perform analyses using multiple imputation in Stata and R. The application of multiple imputation is illustrated with two case studies, in which the decisions required for implementation of the method are examined, highlighting the potential benefits as well as limitations of multiple imputation.

Target audience

This course is suitable for quantitative epidemiologists and applied statisticians working in health research.  It is assumed that participants will have a sound working familiarity with Stata or R, and with statistics to the level of multivariable logistic regression models, prior to the course.


Bring your own laptop (with Stata or R installed).