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SUMMARY:“Extended multivariate generalised linear and non-linear mixed e
 ffect models”  - Dr Michael Crowther\, Biostatistics Research Group\, De
 partment of Health Sciences\, University of Leicester
DTSTART:20171005T133000Z
DTEND:20171005T143000Z
UID:TALK81621@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:Multivariate data occurs in a wide range of fields\, with ever
  more flexible model specifications being proposed\, often within a multiv
 ariate generalised linear mixed effects (MGLME) framework. In this talk\, 
 I’ll describe some current work developing an extended framework\, encom
 passing multiple outcomes of any type\, each of which could be repeatedly 
 measured (longitudinal)\, with any number of levels\, and with any number 
 of random effects at each level. Many standard distributions are described
 \, as well as non-standard user-defined non-linear models. The extension f
 ocuses on a complex linear predictor for each outcome model\, allowing sha
 ring and linking between outcome models in an extremely flexibly way\, eit
 her by linking random effects directly\, or the expected value of one outc
 ome (or function of it) within the linear predictor of another. Non-linear
  and time-dependent effects are also seamlessly incorporated to the linear
  predictor through the use of splines or fractional polynomials. I’ll fu
 rther discuss level-specific random effects distributions and numerical in
 tegration techniques to improve usability\, relaxing the normally distribu
 ted random effects assumption to allow multivariate t-distributed random e
 ffects. I’ll consider some special cases of the general framework\, desc
 ribing some new models in the fields of clustered survival data\, joint lo
 ngitudinal-survival models\, and discuss various potential uses of the imp
 lementation. User friendly\, and easily extendable\, software is provided.
LOCATION:Large Seminar Room\, 1st Floor\, Institute of Public Health\, Uni
 versity Forvie Site\, Robinson Way\, Cambridge
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