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. appreciate 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) 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 3 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 2022-2023.

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

"The theoretical concepts of multiple imputation were well conveyed through pre-recorded lectures which I could scroll back and forth".
Course feedback, June 2023

"I thought that the pace of the course was great and that the different options for the practicals were great. Really enjoyed working through with a demo and getting the feedback in real time. Just the right amount of repetition to reinforce".
Course feedback, June 2023

"The walk throughs in the practicals were really useful, particularly covering Stata and R separately".
Course feedback, June 2023

"The live R demos really brought the concepts to life - definitely a highlight. Seeing it all running, the numbers changing and having a discussion about them really helped develop a more substantive understanding".
Course feedback, June 2023

"The course material and presentations were excellent, it was clear that all the lecturers had experience with teaching this subject. They were very approachable and answered questions thoroughly, providing lots of useful references and even making the effort to come back the following day with more information if a question wasn't completely resolved".
Course feedback, June 2023

"All tutors and moderators were approachable, and I felt comfortable asking questions. It was good to go over a concept or topic multiple times - it made me understand it better".
Course feedback, June 2023

"I think the course was a very good introduction to the topic and did a good job explaining both the theory behind the methods and how to practically implement them".
Course feedback, June 2023

"The course is meticulously organised, and the instructors are incredibly knowledgeable and helpful. Despite the complexity of the topic, they have successfully made the content understandable".
Course feedback, June 2023

"I liked how there were different options for the breakout rooms in the practicals, depending on whether you wanted to work alone or not".
Course feedback, June 2023

"It was incredibly well run and organised. The pace was ideal for such a complex subject area".
Course feedback, June 2023