[R] survval analysis microarray expression data

[David Winsemius]

On Jan 7, 2011, at 3:33 PM, 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.

Trying my university library first without success, a Google search  
then returned this:

http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aoas/1183143730

Open Access aricle and an associated R package, allez. Life is good!

-- 
David.
>
> 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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David Winsemius, MD
West Hartford, CT