1. ## False Discovery Rate

Hi,

Anyone hear familiar with the FDR ?

I have a sample of 80 people, some ill, some healthy. For each one I have a series of let's say for the sake of example, 300 different blood tests. I want to check for each blood test, if there are significant differences between healthy and ill. That means 300 t-tests (or non parametric equivalent). The problem is "over fitting". At the 5% significance level, I expect to receive 15 p values under 0.05, which will be type I errors.

I am trying to use FDR to overcome this problem, I work with the fdrtool package in R. One of the outputs is a vector of "q-values". Are these the values I should refer to instead of the p values ?

any other ideas how to handle such a situation ?

thanks

> fdrtool(p_value_t,statistic="pvalue",plot=TRUE,color.figure=TRUE)
Step 1... determine cutoff point
Step 2... estimate parameters of null distribution and eta0
Step 3... compute p-values and estimate empirical PDF/CDF
Step 4... compute q-values and local fdr
Step 5... prepare for plotting

\$pval
[1] 0.86075075 0.02505751 0.23051528 0.38258383 0.20683905

\$qval
[1] 0.29341273 0.04077265 0.12502865 0.15581219 0.12152287

\$lfdr
[1] 1.0000000 0.1671567 0.2485930 1.0000000 0.1671567

\$statistic
[1] "pvalue"

\$param
cutoff N.cens eta0 eta0.SE
[1,] 0.3854334 1 0.3254326 0.2910758

2. ## Re: False Discovery Rate

do I understand correctly:
http://www.nonlinear.com/support/pro...pq-values.aspx

that I look at q values like p values, a q value bellow my cutoff is significant ?

3. ## Re: False Discovery Rate

In a way. You call anything with a q-value below you predetermined level you want to control for FDR significant. You can't really interpret the q-values after that though. There are more sophisticated ways to estimate your q-values but I can't find any links at the moment. It has to do with estimating the number of true null hypothesis and using that to create a not so harsh penalty on the q-values.

4. ## The Following User Says Thank You to Dason For This Useful Post:

NN_STAT (09-14-2011)

5. ## Re: False Discovery Rate

thanks.

the q values are higher than p values, so will it make sense to choose a predetermined level of above 0.05 ? (assuming that my p value cutoff is 0.05)
I know that there is no rule of how to choose the predetermined level, but still, how do you do it ?

6. ## Re: False Discovery Rate

You choose your cutoff to control the FDR. Whatever you want to control FDR at is your cutoff. So let's say you only want an expected FDR of .05 - choose your cutoff to be .05

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