Question on random effects glm interpretation

Dear David,

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

Best regards,

? 
? >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

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