Dear Terry

thank you very much for this. The number of events in my data is well 
above the suggested size. What concerns me is the fact that a very high 
proportion of probes comes up as significant when I just randomly select 
them. This just seems not be biologically meaningful. Do you know of a 
method that allows you to split your samples into say low-expressors and 
high-expressors? Something like regarding samples with an expression of 
the probe lower than the median-expression minus an constant as 
low-expressors and those with expression higher than median expression 
plus the constant as high-expressors. Do you have any ideas on this?

Best wishes

Kristian

On 07/01/11 20:33, Terry Therneau wrote:
>   For any given pre-specified gene or short list of genes, yes the Cox
> model works fine.  Two important caveats:
>
>   1. Remeber the rule of thumb for a Cox model of 20 events per variable
> (not n=20).  Many microarray studies will have very marginal sample
> size.
>
> 2. If you are looking at many genes then a completely different strategy
> is required.  There is a large and growing literature; I like Newton et
> al, Annals of Applied Statistictis, 2007, 85-106 as an intro; but expect
> to read much more.
>
> Terry Therneau
>
> -------- begin included message ---------
>
> I want to test the expression of a subset of genes for correlation with
> patient survival. I found out that the coxph function is appropriate
> for
> doing this since it works with continuous variables such as Affy mRNA
> expression values.
>
> I applied the following code:
>
> cp<- coxph(Surv(t.rfs, !e.rfs) ~ ex, pData(eset.n0)) #t.rfs: time to
> relapse, status (0=alive,1=dead), ex: expression value (continuous)
>
> The results I get look sensible but I would appreciate any advice on
> the
> correctness and also any suggestions for any (better) alternative
> methods.
>
> Best wishes
>
>
>


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