I don't think your on the right way Marcello,
id year age firstsex_yr
> > 1 1997 15 .
> > 1 1998 16 16
> > 1 1999 17 16
> > 1 2000 18 16
> > 2 1997 12 .
> > 2 1998 13 .
> > 2 1999 14 11
> > 2 2000 15 11
> > 3 1997 16 12
> > 3 1998 17 12
> > 3 1999 18 12
> > 3 2000 19 12
are single failure multiple obs per subject data, so I would suggest:
gen failure = age==firstsex if firstsex<.
sort persnr year
by persnr, sort : gen enterstudy = year==year[1] // if your first year
// isn't 0 one never
// know...
stset age, id(id) failure(failure==1) exit(failure==1)
enter(enterstudy==1)
Hope this helps,
Lars
Am Sonntag, den 16.10.2005, 21:46 -0400 schrieb Marcello Pagano:
> I do not understand your dilemma. Assuming everyone is telling the truth,
> what you seem to have is time to first sex is your outcome of interest with
> the very Victorian identification of "death" as that time. If someone is 17
> at the time of the survey without having had sex, then that is a censored
> observation. So your "time" variable is firstsex_yr if sa==1
> and age if sa==0. So you need to generate a variable
>
> gen time = age
> replace time = firstsex_yr if sa==1
> stset time , failure(sa)
>
> Your hazard should be zero for time < = 10, but that
> depends on your data. You actually do have information back then, assuming
> you have done a decent job of sampling and things have not changed
> that much over the years. (By that I mean that if everyone you question
> is over 12, say, then their experience in the 0 to 12 time period is
> still representative of what is going in those years today.)
>
> Hope this helps,
>
> m.p.
>
> Scott Cunningham wrote:
>
> > On Oct 16, 2005, at 9:01 PM, Nick Cox wrote:
> >
> >> Not my field, but your dummy calculation can
> >> be put more succinctly:
> >>
> >> gen sa = firstsex_yr <= age
> >>
> >> However, safer would be to trap missings:
> >>
> >> gem sa = cond(mi(firstsex_yr, age), ., firstsex_yr <= age)
> >>
> >> Nick
> >
> >
> > Nick,
> >
> > Thanks for helping make the dummies more succinct.
> >
> > Do you think, though, that it is correct to use "age" as the actual
> > duration variable? So, for instance, I have a long dataset like this:
> >
> > id year age firstsex_yr
> > 1 1997 15 .
> > 1 1998 16 16
> > 1 1999 17 16
> > 1 2000 18 16
> > 2 1997 12 .
> > 2 1998 13 .
> > 2 1999 14 11
> > 2 2000 15 11
> > 3 1997 16 12
> > 3 1998 17 12
> > 3 1999 18 12
> > 3 2000 19 12
> >
> > So, by stsetting the data as so:
> >
> > . stset age, failure(sa)
> >
> > where "sa" is an indicator equalling "1" if the person has become
> > sexually active (signalling "death" in this context) and 0
> > otherwise. If I stset the data such that "age" is the duration, have
> > I really made the right decision? Or should I use "year" or should
> > have some other variable that I create to correspond to time that has
> > passed? Because I really want to look at ten periods, initially -
> > from 10 years to 19 years of age. It's a short duration, relatively
> > speaking, and most "exits" occur at 15-17. So I don't actually have
> > data for resopndents for those early, pre-survey, ages - ie, 10-12.
> > So what's the best solution here? Do I create a variable, maybe
> > "time" or "virgin_time", that takes on a value of 1 to 10, and that
> > variable matches up to the years that are covered in the data, and
> > the years not covered?
> >
> > Is this post making sense? I'm mainly just not sure of the proper
> > way to execute this stset command to make use of the information I
> > have in the form I currently have it in.
> >
> >
> > scott
> > *
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>
> *
> * For searches and help try:
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--
Lars E. Kroll
Tutor für EDV & Statistik
Institut für Soziologie
Freie Universität Berlin
KONTAKT:
Email [email protected]
Website www.lkroll.de
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