Re: st: event history analysis with years clustered in individuals

[Steven Samuels <sjhsamuels@earthlink.net>]

I agree with Austin. Just to be clear: sigma_u is a parameter that is  
meaningless for this problem, No interpretation is possible.


On Feb 15, 2009, at 9:22 AM, Austin Nichols wrote:

> Hilde Karlsen <Hilde.Karlsen@hio.no>:
> If you have to use a mixed model as an exercise, and you have no
> compelling reason to choose a particular research question, you should
> ask a different research question where a mixed model is a more
> appropriate model, not apply it blindly to data you know is better
> suited to a survival model.  Why not use the attrition dummy you have
> made as the explanatory variable in a mixed model instead--what other
> variables do you have on the data?
>
> On Sun, Feb 15, 2009 at 8:26 AM, Hilde Karlsen  
> <Hilde.Karlsen@hio.no> wrote:
> > Thank you both for the advice. However, I don't think I can do as you
> > suggest because I have to use a multilevel approach for this essay  
> > (it is an
> > essay for a multilevel course I followed a while ago). I should  
> > probably
> > have been more clear on this issue, and on what my problem really  
> > is. What I
> > am wondering is not which method/command I should use, but how I  
> > am going to
> > interprete the sigma_u estimate when my level 1 variable is years  
> > and my
> > level 2 variable is individuals.
> >
> > As mentioned, I find it more intuitive to grasp the point of separate
> > variance estimates when the levels are schools, classes etc, but  
> > for some
> > reason I have a hard time understanding how I should interpreate the
> > variance estimate sigma_u when the years are clustered in  
> > individuals. How
> > should I interpreate sigma_u when years are clustered in individuals.
> >
> > I asked the professor who was leading the course which command I  
> > should use,
> > and he told me I should use xtmelogit (my advicor told me the same  
> > thing).
> > As he is the one who is going to judge wheter I pass or not on  
> > this essay,
> > it is probably best to follow his advice.
> >
> > I agree that it is a survival model, and I have designed my data  
> > for this
> > type of analysis (i.e. all individuals in the file start out with  
> > 0 on the
> > dependent variable, and when/if they drop out of the nursing  
> > occupation,
> > they receive 1 on the dependent variable. I have no info on which  
> > date/month
> > people drop out; I only have information on which year they drop  
> > out).
> >
> > Regards,
> > Hilde
> >
> >
> > Quoting Steven Samuels <sjhsamuels@earthlink.net>:
> >
> > > Hilde, I agree with Austin's approach. Even if you have only  
> > > months, not
> > > days, of starting and quitting, use that time unit in a survival  
> > > or discrete
> > > survival model.  I recommend  Stephen Jenkins's -hshaz- (get it  
> > > from SSC);
> > > his "model 1" (the "Prentice-Gloeckler model" is the same as that  
> > > fit by
> > > -cloglog-. His model 2 adds unobserved heterogeneity and so may  
> > > be more
> > > realistic (Heckman and Singer, 1984).
> > >
> > > I would not be surprised if prediction equations for of early and  
> > > later
> > > quitting differed. If so, time-dependent covariates or separate  
> > > models for
> > > early and later quitting, would be informative.
> > >
> > > -Steve
> > >
> > > Prentice, R. and Gloeckler L. (1978). Regression analysis of grouped
> > > survival data with application to breast cancer data.  Biometrics  
> > > 34 (1):
> > > 57-67.
> > >
> > > Heckman, J.J. and Singer, B. (1984). A Method for minimizing the  
> > > impact of
> > >         distributional assumptions in econometric models for  
> > > duration data,
> > > Econometrica,         52 (2): 271-320.
> > >
> > >
> > >
> > > > Hilde Karlsen <Hilde.Karlsen@hio.no>:
> > > > Attrition from nursing sounds like a survival model, probably in
> > > > discrete time, using -logit- or -cloglog- with time dummies, not
> > > > -xtmelogit- (see
> > > > http://www.iser.essex.ac.uk/iser/teaching/module-ec968 for a  
> > > > textbook
> > > > and self-guided course on discrete time survival models).  If  
> > > > you have
> > > > T years of data on each individual, all of whom are first-year  
> > > > nurses
> > > > in period 1, and some of whom quit nursing in each of the  
> > > > subsequent
> > > > years, with a variable nurse==1 when a nurse (and zero  
> > > > otherwise), an
> > > > individual identifier id, a year variable year, and a bunch of
> > > > explanatory variables x*, you can just:
> > > >
> > > > tsset id year
> > > > bys id (year): g quit=(l.nurse==1 & nurse==0)
> > > > by id: replace quit=. if l.quit==1 | (mi(l.quit)&_n>1)
> > > > tab year, gen(_t)
> > > > drop _t1
> > > > logit quit _t* x*
> > > >
> > > > and then work up to more complicated models with heterogeneous
> > > > frailty, etc. The main issues are that someone who quit nursing  
> > > > last
> > > > year cannot quit nursing again this year, and people who never quit
> > > > nursing might at some future point that you don't observe, which is
> > > > why you use survival models...
> > > >
> > > > If you know the day they started work and the day they quit, you  
> > > > might
> > > > prefer a continuous-time model (help st).
> > > >
> > > > I've been assuming you had data on people working as nurses, but
> > > > rereading your email, maybe you have data on breastfeeding mothers,
> > > > though I suppose the same considerations apply (though with  
> > > > multiple
> > > > years of data on breastfeeding mothers, there is probably no
> > > > censoring).
> > > >
> > > > On Fri, Feb 13, 2009 at 9:19 AM, Hilde Karlsen  
> > > > <Hilde.Karlsen@hio.no>
> > > > wrote:
> > > > > Dear statalisters,
> > > > >
> > > > > This is probably a stupid question, but I've been searching  
> > > > > around the
> > > > > nets
> > > > > and in books and articles, and I've still not grasped the  
> > > > > concept: When
> > > > > I'm
> > > > > performing a multilevel analysis of attrition from nursing using
> > > > > xtmelogit,
> > > > > and time (year) is the level 1 variable and individuals (id) is  
> > > > > the
> > > > > level 2
> > > > > variable (i.e. years are clustered within individuals; I have a
> > > > > person-year
> > > > > file), how do I formulate the expectation related to this  
> > > > > model? Why is
> > > > > it
> > > > > important to separate between these two levels?
> > > > >
> > > > > I find it more intuitive to grasp the fact that individuals are
> > > > > clustered
> > > > > within schools, and that variables on the school level - as  
> > > > > well as
> > > > > variables on the individual level - may influence e.g. which  
> > > > > grades a
> > > > > student gets.
> > > > >
> > > > > I understand (at least I hope I understand) the point that when  
> > > > > the same
> > > > > individuals are followed over a period of time,  the individual's
> > > > > responses
> > > > > are probably highly correlated, and that this  implies a  
> > > > > violation to
> > > > > the
> > > > > assumption about the heteroskedastic error-terms. As I see it,  
> > > > > I could
> > > > > have
> > > > > used the cluster() - command (cluster(id))to 'avoid' this  
> > > > > violation;
> > > > > however, I have to write an essay using multilevel analysis, so  
> > > > > this is
> > > > > not
> > > > > an option.
> > > > >
> > > > > I don't know if I'm being clear enough about what my problem  
> > > > > is, but any
> > > > > information regarding this topic (how to grasp the concept of  
> > > > > years
> > > > > clustered in individuals) will be greatly appreciated.
> > > > > I'm really sorry for having to ask you such an infantile  
> > > > > question.. My
> > > > > colleagues and friends are not familiar with multilevel  
> > > > > analyses, so I
> > > > > don't
> > > > > know who to turn to.
> > > > >
> > > > > Best regards,
> > > > > Hilde
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