Multiple Imputation for Missing Data
Missing data are almost inevitable in medical research. This leads to a loss of power and potential bias. Multiple imputation is a widely-used and flexible approach for handling missing data.
| Dates | 22 - 24 June 2026 |
|---|---|
| Fee | £750 |
| Format | Online |
| Audience | Open to all applicants (prerequisites apply) |
Advisory
It is not recommend that learners take Advanced Multiple Imputation Methods to deal with Missing Data in the same academic year as Multiple Imputation for Missing Data. The advanced course is deliberately scheduled earlier within each short course programme.
Course profile
This course aims to provide a theoretical and practical introduction to multiple imputation methods for dealing with missing data in straightforward situations.
Please click on the sections below for more information.
This online course consists of a mixture of pre-recorded lectures, short live summary and question and answer sessions, and live computer or discussion practicals.
Participants will have the choice of completing the course in three days (i.e. following the scheduled timetable) or listening to the pre-recorded lectures before the start of the course and attending only the live sessions during the course.
The course is timetabled to start at 09:30 and finish by 17:00 on all three days, with time allocated for coffee breaks and lunch.
By the end of the course participants should be able to:
- recognise the types and patterns of missing data;
- represent a missing data scenario using a causal diagram;
- know when a complete case analysis is likely to be unbiased;
- understand the principles of multiple imputation and be able to outline the process of multiple imputation using chained equations;
- apply multiple imputation methods to deal with missing data in relatively straightforward situations; and
- have a basic knowledge of how multiple imputation methods and results should be presented in journal articles.
The course is intended for statisticians, epidemiologists and other researchers who are, or will be, involved in performing statistical analyses of epidemiological datasets with missing data.
Participants should be familiar with standard regression methods for dichotomous and continuous outcomes beyond the basic introductory level, and be familiar with the core concepts of causal diagrams.
Participants should also be familiar with using either Stata or R as the software package for statistical analyses of the data.
This course will cover:
- an introduction to the problems caused by missing data, including when a complete case analysis is likely to result in bias;
- an introduction to multiple imputation;
- practical sessions performing multiple imputation, including interactions and non-linear associations as well as simple diagnostic checks; and
- a practical session on how to present multiple imputation methods and results in journal articles.
Dr Elinor Curnow and Dr Rosie Cornish, the course organisers, both have expertise in statistical methods for missing data.
To make sure the course is suitable for you and you will benefit from attending, please ensure you meet the following prerequisites before booking:
| Knowledge | Familiarity with either Stata or R. Familiarity with standard regression methods for continuous and binary outcomes beyond a basic level, and familiarity with causal diagrams. |
|---|---|
| Software |
You will be offered the choice to complete practicals in either Stata* or R**. *If opting for Stata: You will need to install Stata (version 13 or later) on your computer prior to attending the course. Internal University of Bristol participants are given access to Stata. Go to Stata Installation Instructions (internal only) for help setting it up before the start of the course. External participants are responsible for providing their own access to Stata, however if you are a student, Stata offer a short term free Student licence (one week). **If opting for R: You can use your own desktop version of R or we will provide a link to Posit Cloud, an interface for R. Go to R Installation Instructions for further information. |
Before booking this course, please make sure you read the information provided above about the target audience and prerequisites. It is important that you have access to the relevant IT resources needed for the course and meet the knowledge prerequisites to ensure you can get the most from the course.
Bookings are taken via our online booking system, for which you must register an account. To check if you are eligible for free or discounted courses please see our fees and voucher packs page. All bookings are subject to our terms & conditions, which can be read in full here.
For help and support with booking a course refer to our booking information page, FAQs or feel free to contact us directly. For available payment options please see: How to pay your short course fees.
Participants are granted access to our virtual learning platform (Blackboard Ultra) 1 to 2 weeks in advance of the course. This allows time for any pre-course work to be completed and to familiarise with the platform.
To gain the most from the course, we recommend that you attend in full and participate in all interactive components. We endeavour to record all live lecture sessions and upload these to the online learning environment within 24 hours. This allows course participants to review these sessions at leisure and revisit them multiple times. Please note that we do not record breakout sessions.
All course participants retain access to the online learning materials and recordings for 5 months after the course.
University of Bristol staff and postgraduate students who do not wish to attend the full course may instead register for access to the 'Materials & Recordings' version of this course: Further information and bookings.
93% of attendees recommend this course*.
*Attendee feedback from 2025.
Here is a sample of feedback from the last run of the course:
“Really clearly built up the concepts with good examples." - Course feedback, June 2025
“The course was really well structured, a nice mix between pre-records and live practical sessions. Information was generally presented clearly and in simple, understandable ways. The examples were really helpful." - Course feedback, June 2025
“The course organisers and facilitators were brilliant and so helpful. R code and live sessions were great. Good to have options to work in a group or independently." - Course feedback, June 2025
"Having parallel sessions for R and STATA users is great as I didn't feel forced to use a software I'm not that familiar with. Explanations were always clear. Good mix between practical sessions and lectures. Interactive quizzes." - Course feedback, June 2025
“I liked the structure of the course (coding earlier, allowing us to work through lectures on our own time) since I needed a bit longer to watch the lectures and take proper notes. I thought the MI paper for pre-reading was appropriate and not too long. I thought the instructors were engaging and easy to approach." - Course feedback, June 2025
“Clear explanations of missing data mechanisms with integration of causal diagrams and hands on Stata practicals for performing multiple imputation and diagnostics." - Course feedback, June 2025
“The course covered a lot over the few days and tutors were very helpful in explaining things. Good that the course included practicals." - Course feedback, June 2025
“I particularly liked the introductory sessions on day 1. They were pitched at a good level for me, and I found them very useful." - Course feedback, June 2025
“I feel that this course has effectively equipped me with the knowledge and practical skills necessary to understand missingness mechanisms in my data and to confidently apply multiple imputation techniques using Stata." - Course feedback, June 2025
“The Stata practicals on multiple imputation and the use of causal diagrams to understand pathways, particularly when complete case analysis or multiple imputation might yield biased estimates, have been invaluable. These will certainly help me apply these skills effectively in my studies." - Course feedback, June 2025
“The process and stages of Multiple imputation were explained so well I feel I have a much deeper insight in to why it's important and how much it can improve analysis. The course has made me very eager to use MI in my next research project as well as equipping me with the skills to do so." - Course feedback, June 2025