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


From   Hilde Karlsen <Hilde.Karlsen@hio.no>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: event history analysis with years clustered in individuals
Date   Sun, 15 Feb 2009 23:17:00 +0100

Yes, maybe I might do that; at least I will write it in my essay to explain why I chose to perform a survival analysis with the hshaz-command. This have been very informative, and the link to the essex-web site on discrete time survival models and the suggestion on the hshaz model 2 was exactly what I needed. Thank you so very much for taking your time helping me.

Regards,
Hilde


Quoting Steven Samuels <sjhsamuels@earthlink.net>:

--

Hilde-

You might explain to the professor that, with survival data, the number of years of observation is itself the (posssibly censored) outcome. Therefore "year" cannot be a level 1 effect in a multilevel model;

-Steve

On Feb 15, 2009, at 3:43 PM, Hilde Karlsen wrote:

Ah. Ok, I see I have to do some serious rethinking when it comes to this essay, then. I guess this to a certain degree explains why I have trouble understanding what sigma_u refers to in this specific analysis. I am wondering if I should forward this e-mail correspondance to the professor who held the course in multilevel techniques, because what I've learned from you today are not in line with what we were told at the course when it comes to this matter. Anyway. Thank you so much for the advice and for answering me.

Regards,
Hilde

Quoting 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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