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From |
"E. Paul Wileyto" <epw@mail.med.upenn.edu> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Wed, 21 Nov 2007 19:37:05 -0500 |

Suppose you had 20 event times, and you stsplit your data using a variable called timept. Timept would take on values from 0 to 19. If you also had a variable for the time at which the screening took place (screentime, also =0..19), then you would simply say:

gen screenstatus=screentime>timepoint.

That should do it.

Paul

Daniel O. Koralek wrote:

Maybe I'm still confused. I can split the data easily using stsplit (splitting at failure time). But I guess the problem I then have, is what do I put in the model (i.e. I don't want to put screen0 - screen3 in the model, i just want one variable called screen...). I guess I could split the data by hand, but it seems a bit of a pain (giving entry and exit times at age at each visit and throwing the appropriate screen(n) variable into the new variable "screen"...--

Thanks,

Dan

From

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

To

statalist@hsphsun2.harvard.edu

Subject

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

Date

Wed, 21 Nov 2007 12:07:44 -0500

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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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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* http://www.stata.com/support/statalist/faq

* http://www.ats.ucla.edu/stat/stata/

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/

*

* For searches and help try:

* http://www.stata.com/support/faqs/res/findit.html

* http://www.stata.com/support/statalist/faq

* http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: discrete time-varying covariate in cox models***From:*"Daniel O. Koralek" <dkoralek@unc.edu>

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