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
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and provide commented, minimal, self-contained, reproducible code.
