Practical use of multiple imputation to handle missing data in Stata

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.

This course is loosely based on our tutorial in Statistics in Medicine.

Lecturers

• Prof Ian White, Institute of Clinical Trials and Methodology (ICTM)
• Prof Angela Wood, University of Cambridge
• Dr Tra My Pham, Institute of Clinical Trials and Methodology (ICTM)

Course aims

• 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.

Target audience

The target audience for this course is researchers needing to analyse incomplete data:

• Attendees are expected to 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 and producing simple graphs
• No prior knowledge of multiple imputation is assumed.

Software required

All participants will need their own laptop running Stata 12 or higher.

External participants are responsible for arranging their own access to Stata. However, students may be eligible for a free short-term (one-week) Stata licence by completing an online application in advance: short term free Student licence (one week).

Data for practicals

It would save time if you could download and install the datasets for the practical sessions before the course.

The data sets for the course practicals are in a zip file, which will be available on Moodle and Teams.

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 that will be emailed to you with the course joining instructions. 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.

Fees

Early Bird Offer

• £161 - Low- and Middle- Income Countries (LMIC).
• £327 - Academics/attendees from not-for-profit organisations.
• £394 - Attendees from or-profit organisations.

Standard fees (after 1 September 2026)

• £175 - Low- and Middle- Income Countries (LMIC).
• £355 - Academics / attendees from not-for-profit organisations.
• £428 - Attendees from for-profit organisations.

Free places - ICTM staff

This course is free for UCL members of staff within ICTM (CRUK-CTC, CCTU, MRC CoRE CTI, UCL InCTU and PRIMENT) although places are limited.  Please contact ictm.cpd@ucl.ac.uk for a validation code to make the registration payment. 

Free places - Low- and Middle- Income countries

The Institute is also able to offer free places to delegates that meet the following criteria on a first come first served basis:

• Resident in Low- or Middle- Income Country (LMIC) - please contact ictm.cpd@ucl.ac.uk for a validation code to make the registration payment.
• Working with UCL InCTU Trial Teams - please ask your InCTU project lead to email ictm.cpd@ucl.ac.uk requesting exemption. 

You may also be interested in:

• Flexible Imputation of Missing Data (2012) by Stef van Buuren
• Multiple imputation and its application (2023) by James Carpenter, Jonathan Bartlett, Tim Morris, Angela Wood, Matteo Quartagno and Mike Kenward
• Other ICTM Short Courses
• LSHTM Missing Data Short Course