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492406-FS2025-0-Winter School: Handling missing data in causal inference and randomised trials





Root number 492406
Semester FS2025
Type of course Course
Allocation to subject (Continuing education) Health Care Organisations
Type of exam not defined
Title Winter School: Handling missing data in causal inference and randomised trials
Description This course is designed for epidemiologists and applied statisticians who have to handle missing data in the analysis of observational or randomised studies.

The aim of the course is to equip you to understand the issues raised by missing data, the likely impact of missing data on a complete records analayis, and when and how to use multiple imputation to reduce bias and recover information.

We will describe the use of directed acyclic graphs (DAGs) to capture the assumptions around missing data and their likely impact on the analysis. Following this, we introduce the concepts of data being missing at random and missing not at random, relating these to the concepts of exchangeability and no unobserved confounding, and to the DAGs.

Using examples, we will introduce multiple imputation and show how it can be used to handle missing values in both causal modelling of observational data (using multivariable regression and also using propensitiy score analyses) and the analysis of randomised controlled trials with missing outcomes.

As the assumptions underlying both complete records analysis and missing at random are untestable, we will describe how sensitivity anlyses can be formulated and implemented using multiple imputation.

The final session of the course will give the opportunity for participants to present their data, and challenges with missing data, for discussion by the tutors and class.

Practicals will use Stata; the majority will also be available in R.
ILIAS-Link (Learning resource for course)
Link to another web site
Lecturers James Robert Carpenter, Institute of Social and Preventive Medicine 
Professor Kate Tilling, Institute of Social and Preventive Medicine ✉
ECTS 1
Recognition as optional course possible No
Grading attended
 
Dates
 
Rooms
 
Students please consult the detailed view for complete information on dates, rooms and planned podcasts.