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Re: st: glm for binomial regression with


From   "Airey, David C" <david.airey@vanderbilt.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: glm for binomial regression with
Date   Thu, 21 Apr 2011 13:03:44 -0500

.

Yes, absolutely, I can.

I was initially thrown by a couple things. First was the usual way the data were being analyzed, by forming percent paralyzed and using a strain by dose ANOVA, and second by not understanding the flexible use of links in glm versus the very flexible xtmelogit command that apparently has one link? I don't understand enough here. Anyway, my clusters are nuisance clusters and a population average model is just fine for that, so xtgee is a good choice that also allows choice of logit or other links. But also I have since read there is often not a great practical difference between use of these links. There again, comparison of predictions in graphs with the data are telling.


> I agree. There is likely to be a difference in magnitude.
> 
> Can you not compare predictions of models, residuals from models, etc.?
> 
> Nick
> 
> On Thu, Apr 21, 2011 at 6:09 PM, Airey, David C
> <david.airey@vanderbilt.edu> wrote:
> > .
> >
> > The fundamental observation was paralysis of locomotory/swimming movement (1 or 0) in a small nematode (worm), called C. elegans. C. elegans is a popular genetic model organism, that surprisingly has appreciable conservation in genetic makeup with other organisms that think about statistics.
> >
> > Part of the procedure involves transfer of worms (n about 10) to a small dish (concave well) with a fixed volume of liquid containing a known concentration of drug. The worms are not touching each other in the well. At a fixed time point, the number of paralyzed worms and the total worms in the dish are recorded. This constitutes a "well" observation. Eight wells were observed in this way for each concentration level per worm strain, and several worms strains are measured, with the goal of discerning dose-response curve shape differences between strains.
> >
> > So we have worm and well replicates and a binary endpoint at the level of the worm replicate. The well constitutes a cluster of worm replicates. With the data arranged with one worm per row, the Stata command below seems reasonable:
> >
> > xtset well
> > xtgee paralyzed i.strain##c.log_dose, family(binomial) link(logit)
> >
> > There are a number of experimental details that could contribute to wells with the same concentration and same worms giving differing results. To the extent these are experimentally minimized, the within well correlation is minimized.
> >
> > With a single worm per well (experimentally inefficient not practical), or lack of within well correlation, we might choose:
> >
> > logit paralyzed i.strain##c.log_dose
> >
> > or if the data are organized as a well per row with the data having variables for number of worms paralyzed and total worms in a well, we can use
> >
> > glm paralyzed i.strain##c.log_dose, family(binomial) link(logit)
> > glm paralyzed i.strain##c.log_dose, family(binomial) link(cloglog)
> >
> > WRT to your comments, are you talking about error in the X variables that are assumed to be measured without error? I think experimental control of X variables is rarely without some error, but the magnitude of that error relative to X variables in observational studies is probably different.
> >

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