[R] survval analysis microarray expression data
dwinsemius at comcast.net
Mon Jan 10 19:13:16 CET 2011
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
> 2. If you are looking at many genes then a completely different
> is required. There is a large and growing literature; I like Newton
> al, Annals of Applied Statistictis, 2007, 85-106 as an intro; but
> to read much more.
Trying my university library first without success, a Google search
then returned this:
Open Access aricle and an associated R package, allez. Life is good!
> Terry Therneau
> -------- begin included message ---------
> I want to test the expression of a subset of genes for correlation
> patient survival. I found out that the coxph function is appropriate
> 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
> correctness and also any suggestions for any (better) alternative
> Best wishes
> R-help at r-project.org mailing list
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
David Winsemius, MD
West Hartford, CT
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