[R-sig-ME] Question on random effects glm interpretation
David Duffy
David.Duffy at qimrberghofer.edu.au
Sun Nov 15 02:59:31 CET 2015
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
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