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

[Hilde Karlsen <Hilde.Karlsen@hio.no>]

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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