1. Statistical Analysis with Missing Data Using Multiple Imputation and Inverse Probability Weighting

Detailled course information (PDF)


Prof. James Carpenter
London School of Hygiene and Tropical Medicine, University of London, United Kingdom Medical Research Council Clinical Trials Unit, Kingsway, London, United Kingdom

Prof. Marcel Zwahlen
Institute of Social and Preventive Medicine (ISPM), University of Bern, Switzerland


Missing data are ubiquitous in 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 and analysis. 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 that combine these
with MI.

This course is aimed at epidemiologists, biostatisticians and other health researchers
with quantitative skills and some experience in statistical analysis. Stata® will be used
for the computer practical sessions, and so familiarity with the package is desirable,
although code and solutions will be provided.

Course objectives

  • To 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)
  • To illustrate the shortcomings of ad-hoc methods for ‘handling’ missing data
  • To introduce multiple imputation for statistical analysis with missing data
  • To 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 practical sessions using Stata®, participants will learn how to
apply the statistical methods introduced in the course to realistic datasets

Course fees

Academic fee: CHF 950
Industry fee: CHF 2’050
SSPH+ fee: only applicable for students of the SSPH+ PhD Program in Public Health