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

[Emmanuel Curis]

Dear Thierry,

Thanks for the hint. 

Just for curiosity, is there any case in which survival analysis will
be equivalent to the Markov model or the GLM(M) model? I remember having
read somewhere that there are links  between survival analysis and
logistic regression, but can't remember exactly which link for the
moment...

Best regards,

On Thu, Nov 12, 2015 at 09:59:18PM +0100, Thierry Onkelinx wrote:
« Dear Emmanuel,
« 
« Maybe a survival analysis is more appropriate for that kind of data.
« 
« Best regards,
« 
« ir. Thierry Onkelinx
« Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
« Forest
« team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
« Kliniekstraat 25
« 1070 Anderlecht
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« 
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« than asking him to perform a post-mortem examination: he may be able to say
« what the experiment died of. ~ Sir Ronald Aylmer Fisher
« The plural of anecdote is not data. ~ Roger Brinner
« The combination of some data and an aching desire for an answer does not
« ensure that a reasonable answer can be extracted from a given body of data.
« ~ John Tukey
« 
« 2015-11-12 18:12 GMT+01:00 Emmanuel Curis <emmanuel.curis at parisdescartes.fr>
« :
« 
« > Dear all,
« >
« > With a colleague, we are discussing about the appropriateness of a
« > random effects mixed effects generalized linear model (more
« > specifically, logistic regression) in a given experimental situation,
« > and wonder about it's correct interpretation.
« >
« > Short version: is it correct to interpret random effects glm, with a
« > single random effect, as hierarchical models as random effects linear
« > models ?
« >
« > Detailed version: our data consist of daily status of a set of
« > patients, the status beeing « Infected » or « Not infected », during a
« > variable period of time. The aim is to see what changes the infection
« > probability.
« >
« > A proposed approach was to use a random effect logistic regression to
« > evaluate this probability, with patient as a random effect.
« >
« > However, we have one concern with that approach: interpretating it in
« > a hierarchical model idea, it seems that for a given patient, the
« > model should be a binomial one, in other words that the set of
« > Bernoulli variables observed each day for a given patient should be
« > independent, identically distributed. But this is obviously not the
« > case here: the variable is 0 until infection occurs (if it occurs),
« > and 1 after.  Consequently, we fear that the probability estimated in
« > the glmm will have no real meaningful interpretation.
« >
« > Are we right with this hierarchical interpretation of the GLMM
« > (logistic) model, or is this fear not justified, and the
« > interpretation of the GLMM more complex, but would lead to correct and
« > interpretable estimations of infection probability?
« >
« > As an alternative approach, we thought about a two-states Markov
« > chain, and working on transition probabilities from the « non-infected »
« > to the « infected » state. It seems to model what happens more
« > closely.  Is there any link between such a model and the logistic GLMM
« > described above, or another kind of GLMM model?
« >
« > And, semantic question, is such a model also in the scope of « random
« > effects » model and could be discussed here, in case help is needed, or
« > is it out of the scope of this list?
« >
« > Thanks in advance for your opinions,
« > Best regards,
« >
« > --
« >                                 Emmanuel CURIS
« >                                 emmanuel.curis at parisdescartes.fr
« >
« > Page WWW: http://emmanuel.curis.online.fr/index.html
« >
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« >

-- 
                                Emmanuel CURIS
                                emmanuel.curis at parisdescartes.fr

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