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Handling missing data in administrative studies: multiple imputation & inverse probability weighting

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Course Information


Course Summary

This ADRC-E course will consider the issues raised by missing data (both item and unit non-response) in studies using routinely collected data, for example electronic health records. Following a review of the issues raised by missing data, we will focus on two methods of analysis: multiple imputation and inverse probability weighting. We will also discuss how they can be used together. The concepts will be illustrated with medical and social data examples.

Target Audience

The course is aimed at quantitative researchers, who have an interest or experience in analysing administrative data. PhD students are also welcome. Detailed technical arguments will not be presented; instead the focus will be on concepts and examples, with participants encouraged to bring their own data for discussion.

This course includes computer workshops, using the statistical software package Stata. Full details of all commands will be given, so no previous experience with Stata is necessary, though it will inevitably be an advantage.


Practical experience using regression modelling (including survival data modelling) and preferably multilevel modelling.

Further course details can be found here.

More information regarding our courses can be found here

Podcast for some of our previous courses can be found here.

Course Code

ADRCE-Training050 Carpenter

Course Dates

9th November 2017 – 10th November 2017

Places Available

Course Leader

Prof James Carpenter
Course Description

Course Outline:

The course covers:

  • Issues raised by missing data in the administrative setting: when is a completerecords analysis sufficient?
  • Shortcomings of ad-hoc methods
  • Introduction to multiple imputation, including algorithms, common pitfalls, reporting and examples
  • Introduction to inverse probability weighting for missing data, and its pros and cons viz-a-viz multiple imputation
  • Combining inverse probability weighting and multiple imputation to improve robustness
  • Strategies for large datasets, including the two-fold multiple imputation algorithm
  • Discussion of participants’ data.


This is a computer lab based training course.


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