Message-ID: <20151117123626.GB1118@info124.pharmacie.univ-paris5.fr>
Date: 2015-11-17T12:36:26Z
From: Emmanuel Curis
Subject: Question on random effects glm interpretation
In-Reply-To: <alpine.LMD.2.00.1511151123290.25288@orpheus.qimr.edu.au>
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