A Gentle Introduction to Causal Inference for Time-to-event Outcomes

Date: Wednesday 04 December 2024, 11.00AM - 2.30PM
Location: Queen's University Belfast - Hybrid with speaker on MS Teams
Queen's University Belfast, Lanyon Building, Room 01. 052
Local Group Meeting
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This is the last RSSNI talk of 2024 and will be given at 1pm GMT by Professor Ruth Keogh of the London School of Hygiene and Tropical Medicine, London, UK.  It will be a hybrid event with the speaker online (MS Teams)  and the audience in Lanyon 01. 052 (which is off the main quadrangle, coming through the main QUB entrance, turn right).

The MS link is here

The talk will follow the AGM and Lunch in Lanyon 01. 052.

All welcome!
 
 
A gentle introduction to causal inference for time-to-event outcomes

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.
 
Professor Ruth Keogh of the London School of Hygiene and Tropical Medicine
 
Contact Gilbert MacKenzie for Northern Ireland Local Group. 
 
 
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