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

[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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