Pre-conference course, June 9th, 11:00-16:00
Causal inference for time-to-event outcomes
with practical applications in R


University of Oslo
This course gives an overview of concepts and methods for estimating causal effects of treatments on censored time-to-event outcomes.
Topics discussed include choices of causal estimands, censoring and identification assumptions, and methods for confounding adjustment. Both point treatments and time-dependent treatment strategies will be covered, and methods such as standardisation/g-computation, inverse probability of treatment weighting and cloning-censoring-and-weighting approaches. Links will be made to the target trial framework.
There will be an emphasis on practical implementation, and the course will combine lectures and demonstrations in R. At the end of the course, participants should be equipped to perform analyses to address questions about causal effects of treatments on their own data.
​
Much of the material will be presented with medical and epidemiological applications in mind, but the methods are equally relevant in other areas, such as demography, social sciences and economics.
Course prerequisites
The course is aimed at researchers and students in biostatistics, statistics, epidemiology and related fields. Familiarity with some basic concepts of survival analysis will be assumed, such as the Kaplan-Meier estimator and Cox regression. Participants should ideally have some prior experience of using R. Knowledge of causal concepts would be beneficial, but we will not assume this.
​​​
​Date and time
Pre-conference course will be held on June 9th, Monday, from 11:00 to 16:00.