Multiple Imputation for 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.
Due to industrial action, this course has been reorganised for 26-27 March 2020
2 days (approximately 12 hours of teaching including lectures, practicals and computer-based practicals).
Registration will start at 9am, the course will finish by 5pm.
This course aims to provide a theoretical and practical introduction to multiple imputation methods to deal with missing data in simple (linear and logistic regression) situations.
The objectives of the course are to:
- recognise the types and patterns of missing data, and when complete case analysis may be unbiased;
- understand and apply multiple imputation methods to deal with missing data;
- discuss how best to present multiple imputation methods and results in journal articles.
Who the course is intended for
The course is intended for statisticians, health economists, 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. Participants should also be familiar with using Stata as the software package for statistical analyses of the data.
The course will include:
- introduction to the problems caused by missing data, complete case analyses and simple multiple imputation;
- introduction to multiple imputation;
- practical sessions performing multiple imputation, including interactions and non-linear associations;
- presenting multiple imputation methods and results in journal articles.
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 will be used during the course.
Sterne et al Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 2009;338:b2393.
White and Carlin. Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values. Stat. in Med. 29; 2920-2931, 2010.
More information on course fees, fee waivers and reduced prices.
Bristol Medical School
39 Whatley Road
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.
Information about accommodation in the area.
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