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 2024.

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

"I really liked the practicals and the order of the material was very logical. The tutors were incredibly helpful and approachable and more than happy to help with questions (however tangential they may have been)" - Course feedback, June 2024

"Good structure of the course - with lectures and exercises, great teaching; plenty of time for questions!" - Course feedback, June 2024

"I found the live sessions with a demonstration really helpful" - Course feedback, June 2024

"Really good course. I liked the mix of live sessions, tutorials, pre-recorded work" - Course feedback, June 2024

"I really liked the balance between pre-record lectures and live practicals. This allowed me to work through the lectures in a flexible way, to avoid getting to overwhelmed with information" - Course feedback, June 2024

"I liked the walk through style practicals, rather than attempting to understand the code on my own" - Course feedback, June 2024

"Really enjoyed the format with a blend of asynchronous and synchronous activities. Question time was good and clear the leaders of the course were engaged and willing to answer a number of questions" - Course feedback, June 2024

"I liked the option to watch the practicals being demo'd, personally I find it more useful to see  commands etc., explained rather than just copying/pasting them in. I also liked the recap at the start of each day and found the session on what to present in a paper particularly useful!" - Course feedback, June 2024

"I appreciated the practical examples and the provided explanations. I enjoyed the structure of the course. Also, enjoyed a lot the final exercise, as it allowed more interactions with other people" - Course feedback, June 2024

"Pre-recorded lecture content was very clear, and well consolidated with the live Q&A and practical sessions. The content well spaced over the three days and pitched at a good level! The walk-through format practicals were very helpful in trialling and discussing concepts" - Course feedback, June 2024

"Brilliant short course. It really takes you step by step in the subject of missing data. I enjoyed the structure of the course, and the chance to immediately apply learned skills" - Course feedback, June 2024

"I learned a lot about the DAGs and its importance. I have clearer ideas about missingness mechanisms and how to tackle them in systematic way. I do hope that this knowledge will be useful when working on my own research" - Course feedback, June 2024