Re: st: discrete time-varying covariate in cox models

["E. Paul Wileyto" <epw@mail.med.upenn.edu>]

Correct me if I'm wrong, but you can use stsplit to manage the data, 
even with covariate values that do not change all at the same time.  You 
can create a split at each analysis timepoint without harm (day1, day2, 
day3...).  It may make your survival dataset bulky, but it will manage 
the risk-set in the appropriate way. 
Paul

Daniel O. Koralek wrote:

> Hi Maarten,
>
> Thanks for your note.  I'm still somewhat confused on the appropriate 
> syntax.  My covariate doesn't necessarily change at each time point 
> (i.e. you could have come to a visit but not actually had a screen).  
> I'm going to show some hypothetical data here (the actual data is 
> confidential).  the screenage variables contain the age at the 
> corresponding study visit date, and the screen variables contain the 
> number of screens undergone up to and including that same study 
> visit.  So, I would like my screening covariate to be equal to the 
> number of screens up to the given analysis time point.
>
> pid    iscase    entryage    exitage    screenage0     screenage1     
> screenage2     screenage3     screen0     screen1     screen2     screen3
> 1    0        52.0        57.2    52.0           53.1        
> 54.0        55.3        1        2        2        3       
> 2    1        52.5        56.1    52.5        53.7        55.0        
> 55.8        1        2        3        4           
> 3    0        52.4        57.6    52.4        53.4        54.5        
> 56.1        1        2         3        3
> ....
>
> if i then used this stset command:
> stset exitage, failure(iscase==1) enter(time entryage) exit(time 
> exitage) scale(1) id(pid)
>
> the following stsplit, isn't going to do it...
> stsplit, at(failure)
>
> i'm totally lost now.  would i need to manually split the data into 
> multiple records by pid, with entry and exitages corresponding to the 
> screenage variables and screen variable being the appropriate screen(n)?
>
> Thanks,
>
> Dan
>
>
>
>
>
> From
>   Maarten buis <maartenbuis@yahoo.co.uk>
> To
>   statalist@hsphsun2.harvard.edu
> Subject
>   Re: st: discrete time-varying covariate in cox models
> Date
>   Tue, 13 Nov 2007 10:26:44 +0000 (GMT)
> --- "Daniel O. Koralek" <dkoralek@unc.edu> wrote:
> > Now, what I would like to do is simply control for a single screen
> > variable that equals the number of screens that occurred up to the
> > analysis time.  THe examples that I have seen using stsplit seem to
> > only use a single change (in this scenario, up to a certain point
> > screen =0 and after screen = 1), not where multiple changes can
> > occur.
>
> If a single change occurs than you create a dummy after -stsplit-, if
> you have multiple changes you add multiple dummies, or if you
> hypothesis a linear effect, a single continuous variable. In all these
> cases -stsplit- doesn't know or cares which scenario applies, it works
> in exactly the same way.
>
> -- Maarten
>
> -----------------------------------------
> Maarten L. Buis
> Department of Social Research Methodology
> Vrije Universiteit Amsterdam
> Boelelaan 1081
> 1081 HV Amsterdam
> The Netherlands
>
> visiting address:
> Buitenveldertselaan 3 (Metropolitan), room Z434
>
> +31 20 5986715
>
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
>
>
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>
>
> Daniel O. Koralek
> Department of Epidemiology/Lineberger Comprehensive Cancer Center
> The University of North Carolina at Chapel Hill
> Chapel Hill, NC  27599-7435
> http://www.unc.edu/~dkoralek/
> dkoralek@unc.edu
>
>
>
>
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--
E. Paul Wileyto, Ph.D.
Assistant Professor of Biostatistics
Tobacco Use Research Center
School of Medicine, U. of Pennsylvania
3535 Market Street, Suite 4100
Philadelphia, PA  19104-3309

215-746-7147
Fax: 215-746-7140
epw@mail.med.upenn.edu
http://mail.med.upenn.edu/~epw/ 
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