RE: st: Parametric survival analysis with competing risks

["Hinchliffe, Sally R." <srh20@leicester.ac.uk>]

Dear Paul,

I think you have misunderstood my work. The slides given in the link attempt to simplify some of the concepts of competing risks. When I presented these I actually went into more detail.

What you are suggesting would not work unless you observe no censoring in your data. So for example, if we had a cohort of patients that are followed for a period of 5 years and there was no censoring within that five year period other than administrative censoring. By administrative censoring I mean a patient being censored if they have not had an event by the end of the 5 year follow-up period.

In this scenario, if we were considering an event of interest and one competing event, then patients that experience the competing event will have their event time inflated to 5 years which would have been their administrative censoring time.

However, in most data we actually observe some censoring. In this case, patients that experience the competing event could actually have had a chance of being censored before the end of the 5 year follow-up period had they not experienced this event. Therefore, by just inflating their time to five years we are not accounting for the censoring distribution in the data.


Kind Regards

Sally Hinchliffe



-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Seed, Paul
Sent: 30 April 2012 17:19
To: statalist@hsphsun2.harvard.edu
Subject: RE: st: Parametric survival analysis with competing risks

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.dy
> > r213.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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