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SUMMARY:Flexible multiple testing using closed testing and Simes - Jelle G
 oeman\, Radboud University Medical Center
DTSTART:20141017T150000Z
DTEND:20141017T160000Z
UID:TALK54668@talks.cam.ac.uk
CONTACT:20082
DESCRIPTION:Classical methods for multiple testing try to avoid false posi
 tive conclusions at all cost. Modern methods based on the False Discovery 
 Rate (FDR)\, popular in omics data analysis\, prefer merely to limit the p
 roportion of false positives among the findings. This is a sensible strate
 gy\, as it gives sufficient control over the potential flood of unreliable
  findings\, while simultaneously avoiding too many false negative results.
  What many users do not realize\, however\, is that these FDR-based method
 s allow much less flexibility in the way the results of these methods can 
 be used. For example\, simple acts of selection among results or aggregati
 on (e.g. from probe level to gene level) may dramatically increase the pro
 portion of false positives. In this talk I propose an alternative way of c
 ontrolling or estimating the proportion of false positives that allows gre
 ater flexibility for the user in later post-processing of the results\, e.
 g. using biological knowledge or bioinformatics tools\, while retaining st
 atistically guaranteed properties. This method is based on a combination o
 f closed testing and the Simes inequality. Special attention is given to t
 he computational challenge of performing closed testing in situations with
  hundreds or thousands of hypotheses.
LOCATION:MR12\,  Centre for Mathematical Sciences\, Wilberforce Road\, Cam
 bridge
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