An Introduction to Dynamic Causal Inference and Multi-state Modelling in Longitudinal Data

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• Aidan O'Keeffe (MRC-BSU, Cambridge)
• Thursday 09 June 2011, 15:15-16:00
• CMS, MR11.

If you have a question about this talk, please contact Elena Yudovina.

Longitudinal data are characterised by repeated measurements being taken over time on subjects/units, and in this longitudinal setting it appears natural to consider causality. Where there exists a causal link between two processes or events, the cause must precede its effect. Hence, it seems plausible that a model which aims to uncover a causal relationship should account for the passage of time between cause and effect. I will discuss the idea that causal relationships in longitudinal data can be inferred from an examination of how processes changing over time may influence each another. Multi-state models offer a way of describing dynamic changes in longitudinal data over continuous time and it is through the use of such models, in conjunction with concepts such as composability, local (in)dependence and the well-known Bradford Hill criteria, that I shall argue that evidence for causal relationships in longitudinal data can be provided. Some of these ideas will be demonstrated using data from the University of Toronto Psoriatic Arthritis clinic, concerning damage progression in the hand joints of psoriatic arthritis patients.

This talk is part of the Statistical Laboratory Graduate Seminars series.

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