Practical use of multiple imputation to handle missing data in Stata
This course takes place over 2 days, on 1st and 2nd February 2018.
Wolfson Centre, Mecklenburgh Square London WC1N 2AP. Further details will be e-mailed to delegates after registration.
Our aim in this course is to provide participants with the ability to analyse their own data using multiple imputation, but also to be aware of the pitfalls and limitations of the technique.
We will give plenty of practical examples from our own experience of analysing data in medical research.
We welcome participants bringing their own data and problems, and one session is dedicated to discussion and possibly "live" analysis of some participants' data.
A tutorial based on this course has appeared in Statistics in Medicine.
- Explain the problems of missing data and the need for methods such as multiple imputation
- Explain how multiple imputation works, with a focus on imputation by chained equations (ICE)
- Explain how multiply imputed data are analysed
- Enable participants to analyse data by multiple imputation in Stata using the commands mi impute chained and mi estimate
- Give participants an awareness of the assumptions underlying multiple imputation and of its limitations.
- Researchers needing to analyse incomplete data
- Should be familiar with running Stata from the command line (i.e. not using menus) at least to the level of fitting a regression model to complete data
- No prior knowledge of multiple imputation is assumed
- Participants should bring their own laptop computer with Stata 12 or newer
- The course would also be suitable as a refresher for people familiar with multiple imputation using ice and mim and wanting to learn about mi impute chained and mi estimate
Note: Stata versions 11 and earlier are not appropriate as the course makes extensive use of mi impute chained which was new in Stata 12.
All participants will need their own laptop running Stata version 12 or newer, as we will be using mi impute chained which was new in Stata 12. Please don't come with a laptop running Stata version 11 or older.
It would save time if you could download and install the datasets for the practical sessions before the course.
Data for practicals
The datasets for the course practicals are in a zip file and should be downloaded from a link that will appear here in due course. We suggest you unzip these to a directory on your hard disk that you create specially for this course.
- How to do this may depend on your browser, etc., but you should be able to get the files just by clicking on the link above. When you see the list of files, click Extract. Then select the directory you want the files to go in. Alternatively, save the zip file to disk and double-click the saved file.
Do and log files for the practicals can be downloaded from a link that will appear here in due course.
The course fees cover copies of the course lectures and practicals, lunch and tea/coffee breaks. Delegates are advised to arrange their own travel and accommodation.
Note for UCL staff: attendance is free for staff from all ICTM units, although the number of places for this groups is limited.
|UCL Staff/Students outside of ICTM||£200|
You may also be interested in
- These books: Flexible Imputation of Missing Data (2012) by Stef van Buuren and Multiple imputation and its application (2012) by James Carpenter and Mike Kenward.
- Other ICTM Short Courses.