Multiple Imputation for Missing Data

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:

1. recognise the types and patterns of missing data;
2. represent a missing data scenario using a causal diagram;
3. know when a complete case analysis is likely to be unbiased;
4. understand the principles of multiple imputation and be able to outline the process of multiple imputation using chained equations;
5. apply multiple imputation methods to deal with missing data in relatively straightforward situations; and
6. 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:

1. an introduction to the problems caused by missing data, including when a complete case analysis is likely to result in bias;
2. an introduction to multiple imputation;
3. practical sessions performing multiple imputation, including interactions and non-linear associations as well as simple diagnostic checks; and
4. 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:

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.

100% of attendees recommend this course*.
*Attendee feedback from 2026.

Here is a sample of feedback from the last run of the course:

“I like how detailed the examples were. I liked that we used DAGs to understand our analysis and imputation models." - Course feedback, June 2026

"Really liked the way Ellie presented and communicated difficult concepts. She is really good at communicating at the right pace and explaining different ideas, really appreciated it." - Course feedback, June 2026

“Liked the start of the course being an intro to missing data and DAGs, that was a bonus as it's not something I've done much of before." - Course feedback, June 2026

“Really liked the way you could watch the tutor go through the practical rather than just be left alone. I gained a lot from what was said in those practicals and how they were explained." - Course feedback, June 2026

“I really enjoyed the instructor-led demonstrations of the practical elements of the course (e.g., in R or Stata) and the option to watch the lectures pre-recorded so that we have time to pause, rewind, and make notes neatly that we can refer back to later." - Course feedback, June 2026

“I think the mix of teaching methods (live, pre-recorded, live computer sessions) was really well balanced and broke things up nicely." - Course feedback, June 2026

“The practical sessions were well set out, and helpful. I enjoyed the menti website with the interactive group questions too." - Course feedback, June 2026

“Comprehensive practical sessions, the sessions were very interactive with sufficient time to actually attempt the practicals." - Course feedback, June 2026

“Very well structured and paced course, very clear and well put together materials." - Course feedback, June 2026

“This is a really thorough course and it helped me to understand all the concepts of MI well." - Course feedback, June 2026

“The practicals were really useful, giving us model example code to work through, as well as highlighting the common pitfalls researchers experience." - Course feedback, June 2026

“I found all elements of the course useful, particularly putting the skills we learned from lectures into practice and being given useful scripts and coding examples that we can adapt in our own work." - Course feedback, June 2026