[ABE-L] Workshop on Longitudinal and Incomplete Data, 24 a 28/11/2014, por Geert Molenberghs (university of Hasselt, Belgium)

[clarice.demetrio em usp.br]

As informações sobre inscrições estão em 

http://fealq.org.br/informacoes-do-evento/?id=209 

----- Mensagem original -----

> De: "clarice demetrio" <clarice.demetrio em usp.br>
> Para: "abe-l ABE" <abe-l em ime.usp.br>, "rbras l"
> <rbras_l em rbras.org.br>
> Cc: "Solange de Assis Paes Sabadin" <solange.sabadin em usp.br>
> Enviadas: Sexta-feira, 26 de Setembro de 2014 9:08:32
> Assunto: Fwd: Workshop on Longitudinal and Incomplete Data, 24 a
> 28/11/2014, por Geert Molenberghs (university of Hasselt, Belgium)

> Título: Workshop on Longitudinal and Incomplete Data
> Palestrante: Geert Molenberghs (University of Hasselt, Belgium)
> Local: Depto de Ciências Exatas, ESALQ/USP, Piracicaba, SP
> Período: 24 a 28/11/2014 (9 as 12 e 14 as 17h)
>  
>  
> Inscrições: http://fealq.org.br/informacoes-do-evento/?id=209

> > Abstract
> 
> > First present linear mixed models for continuous hierarchical data.
> > The focus lies on the modeler’s perspective and on applications.
> > Emphasis will be on model formulation, parameter estimation, and
> > hypothesis testing, as well as on the distinction between the
> > random-effects (hierarchical) model and the implied marginal model.
> > Apart from classical model building strategies, many of which have
> > been implemented in standard statistical software, a number of
> > flexible extensions and additional tools for model diagnosis will
> > be
> > indicated. Second, models for non-Gaussian data will be discussed,
> > with a strong emphasis on generalized estimating equations (GEE)
> > and
> > the generalized linear mixed model (GLMM). To usefully introduce
> > this theme, a brief review of the classical generalized linear
> > modeling framework will be presented. Similarities and differences
> > with the continuous case will be discussed. The differences between
> > marginal models, such as GEE, and random-effects models, such as
> > the
> > GLMM, will be explained in detail. Third, when analysing
> > hierarchical and longitudinal data, one is often confronted with
> > missing observations, i.e., scheduled measurements have not been
> > made, due to a variety of (known or unknown) reasons. It will be
> > shown that, if no appropriate measures are taken, missing data can
> > cause seriously jeopardize results, and interpretational
> > difficulties are bound to occur. Methods to properly analyze
> > incomplete data, under flexible assumptions, are presented. Key
> > concepts of sensitivity analysis are introduced. Throughout the
> > workshop, it will be assumed that the participants are familiar
> > with
> > basic statistical modelling, including linear models (regression
> > and
> > analysis of variance), as well as generalized linear models
> > (logistic and Poisson regression). Moreover, pre-requisite
> > knowledge
> > should also include general estimation and testing theory (maximum
> > likelihood, likelihood ratio). All developments will be illustrated
> > with worked examples using the SAS System. These will be
> > supplemented with practical sessions.
> 

> >  
>
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