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

["Daniel O. Koralek" <dkoralek@unc.edu>]

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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