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BEGIN:VEVENT
DTSTAMP:20260815T063208Z
DTSTART;VALUE=DATE-TIME:20160122T140000
DTEND;VALUE=DATE-TIME:20160122T150000
SUMMARY:CRiSM Seminar
TZID:Europe/London
UID:20160122-094d43454f5f8210014fa7d73c0a6b26@warwick.ac.uk
CREATED:20150907T124544Z
DESCRIPTION:Li Su with Michael J. Daniels (MRC Biostatistics Unit) Bayesi
 an modeling of the covariance structure for irregular longitudinal data 
 using the partial autocorrelation function Abstract: In long-term follow
 -up studies\, irregular longitudinal data are observed when individuals 
 are assessed repeatedly over time but at uncommon and irregularly spaced
  time points. Modeling the covariance structure for this type of data is
  challenging\, as it requires specification of a covariance function tha
 t is positive definite. Moreover\, in certain settings\, careful modelin
 g of the covariance structure for irregular longitudinal data can be cru
 cial in order to ensure no bias arises in the mean structure. Two common
  settings where this occurs are studies with ‘outcome-dependent follow-u
 p’ and studies with ‘ignorable missing data’. ‘Outcome-dependent follow-
 up’ occurs when individuals with a history of poor health outcomes had m
 ore follow-up measurements\, and the intervals between the repeated meas
 urements were shorter. When the follow-up time process only depends on p
 revious outcomes\, likelihood-based methods can still provide consistent
  estimates of the regression parameters\, given that both the mean and c
 ovariance structures of the irregular longitudinal data are correctly sp
 ecified and no model for the follow-up time process is required. For ‘ig
 norable missing data’\, the missing data mechanism does not need to be s
 pecified\, but valid likelihood-based inference requires correct specifi
 cation of the covariance structure. In both cases\, flexible modeling ap
 proaches for the covariance structure are essential. In this work*\, we 
 develop a flexible approach to modeling the covariance structure for irr
 egular continuous longitudinal data using the partial autocorrelation fu
 nction and the variance function. In particular\, we propose semiparamet
 ric non-stationary partial autocorrelation function models\, which do no
 t suffer from complex positive definiteness restrictions like the autoco
 rrelation function. We describe a Bayesian approach\, discuss computatio
 nal issues\, and apply the proposed methods to CD4 count data from a ped
 iatric AIDS clinical trial. *Details can be found in the paper published
  in Statistics in Medicine 2015\, 34\, 2004–2018.
LOCATION:B1.01
CATEGORIES:CRiSM Seminars,Seminars
LAST-MODIFIED:20160201T090935Z
ORGANIZER;CN="":
END:VEVENT
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