[BioC] multtest on human intervention study

[Kevin Dawson]

Hi All,

I am wondering, which statistics is the most reliable, in your experience,
in the following situation:

We're working on a human intervention study dataset with microarrays from
16 patients before and after treatment, 32 samples altogether. Eight of the
patients were treated with a drug and the other 8 were treated with placebo.

The situation is, what we also saw in many other similar cases, that the
patient-to-patient variance is much larger than the treatment effect. As a
solution, we are experessing the RMA(after treatment)-RMA(before treatment)
differences. The resulted 16 samples are analyzed with all the methods in
mulltest plus the maxT/minP methods. None of these methods selected any
genes at p<0.05. At the same time, t-test pointed out about 800
"significant" genes. Unfortunately, these 800 genes are not really the ones
that are biologically plausible.

Q1: Do you have any arguments against the RMA(after)-RMA(before) approach?
RMA is a logarithm, so the difference should express a fold change.

Q2: Do you have another method for similar situations when the
patient-to-patient variance is bigger than the treatment effect?

Q3: Seven tests say NO, is it really a NO? Should I conclude that the
treatment was ineffective?

Q4: Can you suggest another method that is more likely to find a "real"
change in response to treatment.

Thank you in advance for your input. I feel that the basic quesion is
important in all  human studies where multiple samples come from the same
patient. We need a method to control for the patient-to-patient variance.
Statistical and philosphical ideas are all welcome.

Thank you,

Kevin