Hmm...I see. I think I will give a try to the univariate analysis
nonetheless...I intend to catch the p-values for each gene and select the
most significant from these...I have seen it in several papers.
Best Regards,
Eleni
On Feb 13, 2008 2:59 PM, Terry Therneau <[EMAIL PROTECTED]> wrote:
> What you appear to want are all of the univariate models. You can get
> this
> with a loop (and patience - it won't be fast).
>
> ngene <- ncol(genes)
> coefmat <- matrix(0., nrow=ngene, ncol=2)
> for (i in 1:ngene) {
> tempfit <- coxph(Surv(time, relapse) ~ genes[,i])
> coefmat[i,] <- c(tempfit$coef, sqrt(tempfit$var))
> }
>
>
> However, the fact that R can do this for you does not mean it is a good
> idea.
> In fact, doing all of the univariate tests for a microarray has been shown
> by
> many people to be a very bad idea. There are several approaches to deal
> with
> the key issues, which you should research before going forward.
>
> Terry Therneau
>
>
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