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RE: st: Parametric survival analysis with competing risks


From   "Seed, Paul" <paul.seed@kcl.ac.uk>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Parametric survival analysis with competing risks
Date   Mon, 30 Apr 2012 17:19:24 +0100

Dear Enzo, Cameron, 

Thank you for your comments, and reading list. Several the papers are about 
estimating effects of treatments or exposures.  Unbiased estimates are not my concern here.
I am solely interested in arriving at an accurate parametric estimate of the hazard function that 
can be tuned for different test results.  It is important that the resulting survival function 
should correspond to that imagined by the clinicians using it (once I have established what this is); 
and this, it appears, I can achieve by choosing appropriate censoring times for competing events.

I relied mainly on Sally R. Hinchliffe "Competing Risks - What, Why, When
and How?" (http://www2.le.ac.uk/departments/health-sciences/research/biostats/youngsurv/pdf/SHinchliffe.pdf/view)
which draws on Fine & Gray (1999) and Putter (2007) - references given.  The subdistibution hazard 
that she describes appears to have the properties I want.  In particular, (slide 23):

* The difference between cause-specific and 
subdistribution hazards is the risk set.

* For the cause-specific hazard the risk set 
decreases each time there is a death from 
another cause - censoring.

* With the subdistribution hazard subjects 
that die from another cause remain in the 
risk set and are given a censoring time 
that is larger than all event times.

So that's two of the cited references which "recommend to apply the 
ordinary survival regressions treating the subjects with competing 
events as censored at the end of the study period."

Thanks again, 


Paul Seed




Date: Sun, 29 Apr 2012 08:49:55 -0400
From: Cameron McIntosh <cnm100@hotmail.com>
Subject: RE: st: Parametric survival analysis with competing risks

Correct -- the results will be biased if you use a naive Kaplan-Meier (censoring out the competing events as you say). 
Cox or Fine-Gray models are preferable in this case, but not perfect. Happy reading, Cam

> Date: Sun, 29 Apr 2012 08:40:42 +0200
> From: enzo.coviello@tin.it
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: Parametric survival analysis with competing risks
> 
> Hi,
> 
> thanks for the references.
> Let me consider that none of the cited references recommend to apply the 
> ordinary survival regressions treating the subjects with competing 
> events as censored at the end of the study period.
> 
> Enzo
> 
> 
> 
> Il 28/04/2012 22.27, Cameron McIntosh ha scritto:
> > Enzo,
> > I also suggest you take a look at:
> >
> > Andersen, P.K., Geskus, R.B., de Witte, T.,&  Putter, H. (2012). Competing risks in epidemiology: possibilities and pitfalls. International Journal of Epidemiology, Advance Access.http://ije.oxfordjournals.org/content/early/2012/01/08/ije.dyr213.abstracthttp://192.38.117.59/~pka/avepi11/Research_Report_11-2.pdf
> >
> > Dignam, J.T., Zhang, Q.,&  Kocherginsky, M. (2012). The Use and Interpretation of Competing Risks Regression Models. Clinical Cancer Research, 18, 2301-2308.
> >
> > Dignam, J.T.,&  Kocherginsky, M. (2008). Choice and Interpretation of Statistical Tests Used When Competing Risks Are Present. Journal of Clinical Oncology, 26(24), 4027-4034. 
> >
> > Fine, J. P.,&  Gray, R.J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496-509.
> >
> > Gichangi, A.,&  Vach, W. (2005). The analysis of competing risks data: a guided tour. Statistics in Medicine, 132(4), 1-41.
> >
> > Lambert, P. C., Dickman, P. W., Nelson, C. P.,&  Royston, P. (2010). Estimating the crude probability of death due to cancer and other causes using relative survival models. Statistics in Medicine, 29(7-8), 885-895.
> >
> > Williamson, P. R.,  Kolamunnage-Dona, R.,&  Smith, C.T. (2007). The influence of competing-risks setting on the choice of hypothesis test for treatment effect.  Biostatistics, 8(4), 689-694.http://biostatistics.oxfordjournals.org/content/8/4/689.full
> >
> > Putter, H., Fiocco, M.,&  Geskus, R.B. (2007). Tutorial in biostatistics: competing risks and multi-state models. Statistics in Medicine, 26(11), 2389-2430.http://web.inter.nl.net/users/rgeskus/CompRisk.pdf
> >
> > Cam


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