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CATEGORIES:MRC Biostatistics Unit Seminars
SUMMARY:Semi Markov models under panel observation - Andr
ew Titman\, University of Lancaster
DTSTART;TZID=Europe/London:20130514T143000
DTEND;TZID=Europe/London:20130514T153000
UID:TALK44517AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/44517
DESCRIPTION:Multi-state models are widely used in event histor
y analysis. Often the state of the process is only
known at a set of discrete\, potentially unequall
y spaced and subject specific\, examination times
leading to panel data. Most analyses for panel dat
a assume a Markov model\, but we may instead wish
to allow the transition intensities to depend on t
he time spent in the current state leading to a se
mi-Markov model. The likelihood for general semi-M
arkov models is somewhat intractable. This talk fo
cuses on semi-Markov models with phase-type sojour
n distributions which allow an aggregated (or hidd
en) Markov representation making computation simpl
er. Two main approaches can be considered. Firstly
\, the states in the model can be assumed to have
phase-type distributions directly [1]. Alternative
ly\, phase-type distributions approximations to pa
rametric distributions can be used to build an app
roximate likelihood for Weibull or Gamma semi-Mark
ov models [2]. In either case\, the addition of mi
sclassification of the disease states can be incor
porated relatively easily. The methods are illustr
ated on chronic disease data from post-lung-transp
lantation patients.\n\n[1] Titman A.C.\, Sharples
L.D. Semi-Markov models with phase-type sojourn di
stributions. Biometrics. 2010. 66 (3): 742-752.\n[
2] Titman A.C. Estimating parametric semi-Markov
models from panel data using phase-type approximat
ions. Statistics and Computing. 2012. Online First
.\n\n\n
LOCATION:Large Seminar Room\, 1st Floor\, Institute of Pub
lic Health\, University Forvie Site\, Robinson Way
\, Cambridge
CONTACT:Dr Jack Bowden
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