Thanks to Ben Jann for the detailed response
Nikos Pantazis
Biostatistician
--- "Jann, Ben" <[email protected]> wrote:
> Assume -id- indicates the subjects (in all
> datasets), -a.dta- contains your demographic data,
> -b.dta- and -c.dta- are the marker1 and marker2
> datasets, respectively. The variables -m1- and -m2-
> contain the values of the measurements of the
> markers 1 and 2, respectively. -t- ist the time of
> measurement in both marker datasets, -time- is the
> time of the event (in a.dta), -fail- indicates
> failure event vs. censored, x1, x2 ... are
> covariates. To summarize:
>
> a.dta: id time fail x1 x2 ...
> b.dta: id t m1
> c.dta: id t m2
>
> If you only want to use one marker, do the
> following:
>
> . use b
> . sort id t
> . save bb, replace
> . use a
> . sort id
> . joinby id using bb
> . sort id t /* just to be sure */
> . by id: replace time=t[_n+1] if _n!=_N
> . by id: replace fail=0 if _n!=_N
>
> If the measurement of the marker has occured after
> time zero, you can either assume that the marker has
> been constant up to the first measurement (A) or
> treat the case to be left censored (B).
>
> A:
>
> . stset time, id(id) failure(fail)
> . stcox m1 x1 x2 ...
>
> B:
>
> . stset time, id(id) failure(fail) enter(t)
> . stcox m1 x1 x2
>
> If you need to use both markers, things are a little
> more complicated. First combine b.dta and c.dta,
> fill in some gaps and joinby (assuming that there
> are no simultaneous measurements of marker1 and
> marker2; you'd have to modify the code if there are
> simultaneous measurements):
>
> . use b
> . append using c
> . sort id t
> . by id: replace m1=m1[_n-1] if m1==.
> . by id: replace m2=m2[_n-1] if m2==.
> . save bc, replace
> . use a
> . sort id
> . joinby id using bc
> . sort id t /* just to be sure */
> . by id: replace time=t[_n+1] if _n!=_N
> . by id: replace fail=0 if _n!=_N
>
> Then, again the two possibilities A (constant) and B
> (left censored):
>
> A:
>
> . gsort id -t
> . by id: replace m1=m1[_n-1] if m1==.
> . by id: replace m2=m2[_n-1] if m2==.
> . sort id t
> . stset time, id(id) failure(fail)
> . stcox m1 m2 x1 x2 ...
>
> B:
>
> . stset time, id(id) failure(fail) enter(t)
> . stcox m1 m2 x1 x2 ...
>
> I hope this works. Didn't test it.
>
> ben
>
>
> > -----Urspr�ngliche Nachricht-----
> > Von: n p [mailto:[email protected]]
> > Gesendet: Donnerstag, 31. Juli 2003 13:29
> > An: [email protected]
> > Betreff: st: Survival with time varying covariates
> >
> >
> > Hi Stata users,
> >
> > I need your help in the following problem. I want
> to
> > perform a survival analysis (via Cox PH models)
> where
> > some covariates (e.g. demographics) are constant
> over
> > study time while others (Marker1 and Marker2) are
> > periodically measured (usually after time zero
> and
> > not simultaneously). Thus I have 3 datasets
> >
> > a) 1 record per subject, with time of event or
> > censoring and other not time varying covariates
> > b) n1 records per subject for repeated
> measurements of
> > marker1 (time and value of measurement)
> > c) n2 records per subject for repeated
> measurements of
> > marker2 (time and value of measurement)
> >
> > The effect of both markers on the hazard is the
> main
> > question in this study.
> >
> > I checked the manual's example "Stanford heart
> > transplant data" and the "stsplit" command. I also
> > checked "stegen" and "stcoxtvc" using "findit time
> > varying covariates" but I am still puzzled with
> this.
> > Is there an easy way to prepare the data at least
> for
> > one of the two markers? How can I deal with the
> > unknown markers' values between time zero and
> their
> > first measurement?
> >
> > I am using Stata 7 on a 2.4 GHz 512 RAM WinXP
> machine.
> >
> > Thanks in advance for any help.
> >
> > Nikos Pantazis
> > Biostatistician
> >
> >
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