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Re: st: Parametric survival aalysis with competing risks


From   "Roger B. Newson" <r.newson@imperial.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Parametric survival aalysis with competing risks
Date   Wed, 25 Apr 2012 20:57:50 +0100

What exactly do you mean by "The goal is the prediction of spontaneous preterm labour"? Did you want relative hazard rates for spontaneous pre-term labour, or a predictive score for spontaneous pre-term labour, or an estimate for the probability of spontaneous pre-term labour that you can quote to pregnant women, assuming that they do not opt for induction of labour or surgery? Or something else?

Best wishes

Roger


Roger B Newson BSc MSc DPhil
Lecturer in Medical Statistics
Respiratory Epidemiology and Public Health Group
National Heart and Lung Institute
Imperial College London
Royal Brompton Campus
Room 33, Emmanuel Kaye Building
1B Manresa Road
London SW3 6LR
UNITED KINGDOM
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Email: r.newson@imperial.ac.uk
Web page: http://www.imperial.ac.uk/nhli/r.newson/
Departmental Web page:
http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/

Opinions expressed are those of the author, not of the institution.

On 25/04/2012 13:04, Paul Seed wrote:
Dear Statalist,

The -st- suite contains two commands for parametric survival analysis
regression;
and for survival analysis with competing risks (using a Cox's model):
-streg , model()-, and -stcrreg- respectively.
Is there any way of combining the two features?

I need a parametric method as I want to put the formulae into an
prediction probability calculator, and the problem really does
have competing risks.  The goal is the prediction of spontaneous
preterm labour, with Induction of labour or surgery as competing risks.

As far as I can see, -findit competing risk- does not produce
any user-written command that does this, and I can see nothing relevant
in Statalist archives.

Alternatively, is there a better way of allowing for competing risks in a
parametric model than by simply treating subjects as censored at the time of

the competing event?

Best wishes,


Paul T Seed
Division of Women's Health, King's College London
Women's Health Academic Centre KHP
020 7188 3642,



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