Multiple Imputation for Missing Data

An online short course

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.

Course date 10 - 11 May 2021
Course fee £440
Course Organisers Dr Rosie Cornish & Dr Rachael Hughes

Prerequisites

Please ensure you meet the following prerequisites before booking:

Knowledge Familiarity with Stata.
Familiarity with standard regression methods for continuous and binary outcomes beyond a basic level.
Software You must have Stata (version 13 or later)* installed in advance of the course.
*Internal University of Bristol participants will be provided with access to Stata version 16 on the first day of the course.

Course format

The course will consist of a mixture of pre-recorded lectures, short live summary and question and answer sessions, and live computer practicals.

Participants will have the choice of completing the course in two days (i.e., following the timetable as scheduled) or listening to the pre-recorded lectures before the start of the course and attending only the live sessions on the day of the course.

The course is timetabled to start at 09:30 and finish by 17:00 both days, with time allocated for coffee breaks and lunch.

Course objectives

By the end of the course, participants will:

  1. be able to recognise the types and patterns of missing data;
  2. know when a complete case analysis is likely to be unbiased;
  3. understand the principles of multiple imputation and be able to outline the process of multiple imputation using chained equations;
  4. be able to apply multiple imputation methods to deal with missing data in relatively straightforward situations;
  5. know how multiple imputation methods and results should be presented in journal articles.

Who the course is intended for

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. Participants should also be familiar with using Stata as the software package for statistical analyses of the data.

Course outline

The course will include:
  1. An introduction to the problems caused by missing data, complete case analyses and simple multiple imputation.
  2. An introduction to multiple imputation.
  3. Practical sessions performing multiple imputation, including interactions and non-linear associations.
  4. A practical session on how to present multiple imputation methods and results in journal articles.

Recommended reading

Sterne et al Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 2009;338:b2393.

Online Course Bookings


Bookings are open for online courses running in 2021.

The content of the course was very comprehensive and I felt like the key Missing Imputation principles were addressed at a good level of detail.

Course feedback, March 2019

Coronavirus (COVID-19)

We may need to make responsive changes to our courses at short notice in order to follow the latest Public Health, Government and University guidance on coronavirus (COVID-19).

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