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SUMMARY:"Dynamic prediction of survival using landmarking in large healthc
 are databases\, with an application in cystic  fibrosis" - Dr Ruth Keogh\,
  London School of Hygiene and Tropical Medicine
DTSTART:20170119T143000Z
DTEND:20170119T153000Z
UID:TALK69773@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:In ‘dynamic’ prediction of survival we make updated predic
 tions of individuals’ survival over time as new information becomes avai
 lable about their health status. Landmarking is an attractive and flexible
  method for dynamic prediction.\n\nLarge observational patient databases p
 rovide longitudinal data on clinical measurements and present opportunitie
 s to develop ‘personalised’ dynamic predictions of survival. This work
  is motivated by the aim of developing dynamic prediction models for survi
 val in people with cystic fibrosis\, one of the most common inherited life
 -shortening diseases\, using the US Cystic Fibrosis Patient Registry\, whi
 ch contains longitudinal clinical data on over 40\,000 people. I will disc
 uss some of the challenges faced in making dynamic predictions of survival
  in this data and show how they can be addressed in the landmarking framew
 ork. I will also show some comparisons between landmarking and the alterna
 tive approach of joint modelling of longitudinal and survival data and hop
 efully convince you that landmarking has a number of advantages.
LOCATION:Large  Seminar Room\, 1st Floor\, Institute of Public Health\, Un
 iversity Forvie Site\, Robinson Way\, Cambridge
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