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Statistical analysis with missing data using multiple imputation and inverse probability weighting


Course dates: 21 - 23 June 2017

A short course taught in London by statisticians from the Department of Medical Statistics, and part of the School's Centre for Statistical Methodology.

Missing data frequently occurs in both observational and experimental research. They lead to a loss of statistical power, but more importantly, may introduce bias into the analysis. In this course we adopt a principled approach to handling missing data, in which the first step is a careful consideration of suitable assumptions regarding the missing data for a given study. Based on this, appropriate statistical methods can  be identified that are valid under the chosen assumptions. The course will focus particularly on the practical use of multiple imputation (MI) to handle missing data in realistic epidemiological and clinical trial settings, but will also include an introduction to inverse probability weighting methods and new developments which combine these with MI.

During the course participants will receive a copy of the recently published book "Multiple imputation and its application" by Carpenter and Kenward.

Who should apply?

Epidemiologists, biostatisticians and other health researchers with strong quantitative skills and experience in statistical analysis. Stata will be used for the computer practicals, and so familiarity with the package is highly desirable, although full Stata code and solutions will be provided.

Course fee

The fee for 2017 is £815

Course objectives

Course Content

The course will:

  • provide an introduction to the issues raised by missing data, and the associated statistical jargon (missing completely at random, missing at random, missing not at random)
  • illustrate the shortcomings of ad-hoc methods for 'handling' missing data
  • introduce multiple imputation for statistical analysis with missing data
  • compare and contrast this with other methods, in particular inverse probability weighting and doubly robust methods, and
  • to introduce accessible methods for exploring the sensitivity of inference to the missing at random assumption

Through computer practicals using Stata, participants will learn how to apply the statistical methods introduced in the course  to realistic datasets.

Course Certificate and Assessment

There will be no formal assessment, but participants will receive a Certificate of Attendance.

How to apply

Applying for this course



The student is responsible for obtaining any visa or other permissions to attend the course, and is encouraged to start the application process as early as possible as obtaining a visa for the UK can sometimes take a long time. The Short Courses team, in the Registry, can provide supporting documentation if requested.

Accommodation and meals

A list of hotels and other accommodation located in the vicinity of the School can be supplied on request to the Registry. Lunch can be purchased from the School's Refectory in the Keppel Street building or the cafe on the Tavistock Place building. Evening meals are not catered for at the School, but there is a large choice of restaurants, cafes and shops nearby.


The London School of Hygiene & Tropical Medicine is committed to improving global health through its programme of short and full-time postgraduate study.

Please note:

  • If you have been offered a place on the course you will not be able to register without bringing formal ID (Passport) and without having obtained the correct visa.
  • It is essential that you read the current visa requirements for short course students. To view this information please click here.
  • The School may cancel courses two weeks before the first day of the course if numbers prove insufficient.  In those circumstances, course fees will be refunded.
  • The School cannot accept responsibility for accommodation, travel and other losses incurred as a result of the course being cancelled.

Admission status

Please complete the online application form

Fees 2017

£815 - payable by 17 May 2017

Course dates

21 - 23 June 2017

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