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SUMMARY:An Introduction to Dynamic Causal Inference and Multi-state Modell
 ing in Longitudinal Data - Aidan O'Keeffe (MRC-BSU\, Cambridge)
DTSTART:20110609T141500Z
DTEND:20110609T150000Z
UID:TALK30510@talks.cam.ac.uk
CONTACT:Elena Yudovina
DESCRIPTION:Longitudinal data are characterised by repeated measurements b
 eing 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. Henc
 e\, it seems plausible that a model which aims to uncover a causal relatio
 nship should account for the passage of time between cause and effect. I w
 ill discuss the idea that causal relationships in longitudinal data can be
  inferred from an examination of how processes changing over time may infl
 uence each another. Multi-state models offer a way of describing dynamic c
 hanges in longitudinal data over continuous time and it is through the use
  of such models\, in conjunction with concepts such as composability\, loc
 al (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 Uni
 versity of Toronto Psoriatic Arthritis clinic\, concerning damage progress
 ion in the hand joints of psoriatic arthritis patients.
LOCATION:CMS\, MR11
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