[R-sig-ME] Question on random effects glm interpretation

[Emmanuel Curis]

Dear David,

Thank you for the clarification and pointing out these R packages. We
will dig into this...

Best regards,

On Sun, Nov 15, 2015 at 11:59:31AM +1000, David Duffy wrote:
« On Fri, 13 Nov 2015, Thierry Onkelinx wrote:
« 
« >Maybe a survival analysis is more appropriate for that kind of data.
« >>
« >>Detailed version: our data consist of daily status of a set of
« >>patients, the status being « Infected » or « Not infected », during a
« >>variable period of time. The aim is to see what changes the infection
« >>probability.
« 
« As Thierry suggested, this type of dataset (incidence of infection) can be
« modelled as a recurrent events survival analysis.  These can be represented
« as in a discrete time framework as a poisson GLMM, or as Cox or parametric
« mixed effects survival models. One example might be various analyses of the
« "kidney" dataset of McGilchrist and Aisbett that is included in the BUGS
« manual. In R as:
« 
« brms::kidney            Infections in kidney patients
« frailtyHL::kidney       Kidney Infection Data
« INLA::Kidney            Kidney infection data
« survival::kidney
« 
« The Markovian model appears in bivariate survival analyses covering time to
« infection and time to recovery - each may have different relevant risk
« factors - the random effect (frailty) for each individual links them
« together appropriately. You will probably also want time varying covariates.
« 
« Cheers, David Duffy.
« 
« 
« | David Duffy (MBBS PhD)
« | email: David.Duffy at qimrberghofer.edu.au  ph: INT+61+7+3362-0217 fax: -0101
« | Genetic Epidemiology, QIMR Berghofer Institute of Medical Research
« | 300 Herston Rd, Brisbane, Queensland 4006, Australia  GPG 4D0B994A


-- 
                                Emmanuel CURIS
                                emmanuel.curis at parisdescartes.fr

Page WWW: http://emmanuel.curis.online.fr/index.html