Advanced Multiple Imputation Methods to deal with Missing Data

Coronavirus (COVID-19) information

The Short Course Programme in Population Health Sciences has been temporarily suspended.


We anticipate opening bookings in late November 2020.

Information on this page relates to the last run of the course and is for reference only. 

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We may need to make responsive changes to our future programme to follow the latest Public Health, Government and University guidance on coronavirus (COVID-19).

Please be aware that all information about short courses planned for 2021 is provisional and subject to change.

Course dates

17 May 2019

Course duration

1 day (approximately 6 hours of teaching).
Registration will start at 8.45am, the course will finish by 5pm.

Course tutors

Dr Rachael HughesDr Elsa Marques(course organisers)

Well thought through, well-structured and well presented. Good to extend coverage from the first Multiple Imputation course to the more complex situations covered here.

Course feedback, May 2019

Course aims 

This course aims to extend the application of multiple imputation methods to deal with missing data in complex analyses, including longitudinal models and clustered data.

Course objectives

By the end of the course participants will be able to explore advanced multiple imputation methods when the substantive analysis of interest:

  1. includes non-normally distributed continuous variables, interactions and non-linear term
  2. is a survival model 
  3. is a multilevel model

Who the course is intended for

The course is intended for statisticians, health economists, epidemiologists and other researchers who are involved in performing statistical analyses of epidemiological datasets with missing data. Participants should be familiar with: the concept of multiple imputation, and have used it in practice; standard regression methods for dichotomous and continuous outcomes beyond the basic introductory level; and using Stata as the software package for statistical analyses of the data.

Course outline

This course builds on prior knowledge of multiple imputation for dealing with missing data, and extends this to the application of multiple imputation in complex analyses.

The course will include: 

  1. brief revision of theory and practice of multiple imputation in simple scenarios
  2. guidance on specification of the functional form of the imputation model when the substantive analysis of interest includes non-normally distributed continuous variables, interactions and non-linear terms
  3. Multiple imputation and survival analyses
  4. Multiple imputation and analyses of longitudinal data and clustered data
  5. Interactive practical sessions

Please note: Practical sessions of this course will be held in a computer lab, so you will not need to bring a laptop. Stata 15, Stat-JR, REALCOM-Impute and R will be used during the course.

Course fee


More information on course fees, fee waivers and reduced prices.

Course venue

Bristol Medical School
Canynge Hall
39 Whatley Road
United Kingdom

Map and directions

Course refreshments

We provide morning and afternoon refreshment breaks, including tea and coffee, biscuits and fresh fruit.

If you have specific dietary needs we ask that you let us know in advance.

Lunch is not included. There are a range of local cafes and supermarkets nearby for students to purchase lunch. 

Related short courses


For further information please email

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