Causal inference methods for estimating effects of treatments or other interventions on health outcomes have seen rapid and extensive developments in recent years. This talk aims to provide a introduction to methods at our disposal for estimating the causal effects of treatments on time-to-event outcomes using observational healthcare data. Methods discussed will include marginal structural models, inverse probability weighting, the g-formula, and doubly-robust approaches such as targeted maximum likelihood estimation, which can incorporate machine learning methods. Traditional methods for investigations into effects of treatments on time-to-event outcomes, such as Cox regression, are sometimes stated as not being suitable for use in causal inference. However, I will discuss how these techniques still form an important part of the toolkit for causal inference for time-to-event outcomes. A running example will be used to illustrate the methods.

Progamme for December 4th 2024.
11:00 AGM
12:15 Lunch*
13:00 Talk
*RSVP to Secretary;  R.McDowell@ulster.ac.uk

See previously recorded talks here.
Write-ups of talks from 2020-2023 here.