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PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3-
 M3//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20151217T123735Z
DTSTART:20160215T150000Z
DTEND:20160215T163000Z
SUMMARY:Seminar: Insights into the analysis of recurrent events
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}hpm-iia8fgb
 d-29nac2
DESCRIPTION:'''Host:''' Institute of Population Health\n\n'''About the ev
 ent:'''\n\nMany chronic diseases are characterised by nonfatal recurrent
  events. Examples of such include asthma attacks in asthma\, epileptic s
 eizures in epilepsy and hospitalisations for worsening condition in hear
 t failure. Analysing all of these repeat events within individuals is mo
 re representative of disease progression and more accurately estimates t
 he effect of treatment on the true burden of disease. \n\nThis talk will
  start by outlining the different methods that are available for analysi
 ng recurrent event data. We shall illustrate and compare various methods
  of analysing data on repeat hospitalisations using simulated data and d
 ata from major trials in heart failure.\n\nAn increase in heart failure 
 hospitalisations is associated with a worsening condition and a subseque
 nt elevated risk of cardiovascular death\, meaning that subjects may die
  during follow-up. A comparison of heart failure hospitalisation rates\,
  between treatment groups\, can be confounded by this competing risk of 
 death and any analyses of recurrent events must take into consideration 
 informative censoring that may be present. The Ghosh and Lin (2002) non-
 parametric analysis of heart failure hospitalisations takes mortality in
 to account whilst also adjusting for different follow-up times and multi
 ple hospitalisations per patient. Another option is to treat the inciden
 ce of cardiovascular death as an additional event in the recurrent event
  process and then adopt the usual analysis strategies that will be prese
 nted and discussed in this session. An alternative approach is the use o
 f joint modelling techniques to obtain estimates of treatment effects on
  heart failure hospitalisation rates whilst allowing for informative cen
 soring.\n\nJoint modelling techniques are appropriate when analysing rat
 es of recurrent events whilst allowing for association with a potentiall
 y informative dropout time\, or when each of the outcomes is of scientif
 ic importance to the investigators and the dependence between the two pr
 ocesses needs to be accounted for. One approach to joint modelling is ra
 ndom effects models\, which assume that the recurrent hospitalisations a
 nd time-to-death are conditionally independent given a latent variable. 
 Models of this kind are intuitively appealing as they can give a tangibl
 e interpretation that an individual’s independent frailty term measures 
 their underlying\, unobserved severity of illness\, which proportionatel
 y affects both their heart failure hospitalisation rate and their time-t
 o-death (or CV death). Joint models allow distinct treatment effects to 
 be estimated for each of the processes\, whilst taking into account the 
 association between the two.\n\nThis talk shall outline the different me
 thods available for analysing recurrent events in the presence of depend
 ent censoring and the relative merits of each method shall be discussed.
  In addition\, data from multiple large scale clinical trials in cardiov
 ascular disease shall be used to illustrate the application of these met
 hods.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:G306A\, Jean McFarlane Building \, Oxford Rd\, Manchester\, M139
 PY
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