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

  • N/A
    Application Deadline
  • 3 days
  • Tuition
    Tuition (Module)
    Tuition (Module)
  • English (take IELTS)
  • Overview
  • Programme outline
  • Key facts
  • Admission requirements
  • Fees and funding


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.

Detailed Programme Facts

  • More details: Course dates 21 - 23 June 2017
  • Full-time duration 3 days
  • Study intensity Full-time
  • Languages
    • English
  • Delivery mode
    On Campus
  • Entry Level

Programme Structure

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


  • Dr James Carpenter
  • Karla Diaz-Ordaz

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. 

Tuition Fee Per Module

  • GBP 815 International
  • GBP 815 EU/EEA

payable by 17 May 2017


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