Hi Eleni,
The problem of this approach is easily explained: Under the Null
hypothesis, the P values
of a significance test are random variables, uniformly distributed in
the interval [0, 1]. It
is easily seen that the lowest of these P values is not any 'better'
than the highest of the
P values.
Best wishes,
Matthias
Eleni Christodoulou schrieb:
> 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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>
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