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SUMMARY:Sequential stopping for high-throughput experiments - Mueller\, P 
 (Texas at Austin)
DTSTART:20110810T134500Z
DTEND:20110810T143000Z
UID:TALK32304@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:In high-throughput experiments sample size is typically chosen
  informally. Although formal sample size calculations have been proposed\,
  they depend critically on prior knowledge. We propose a sequential strate
 gy which\, by updating knowledge when new data is available\, depends less
  critically on prior assumptions. Compared to fixed sample size approaches
 \, our sequential strategy stops experiments early when enough evidence ha
 s been accumulated\, and recommends continuation when additional data is l
 ikely to provide valuable information. The approach is based on a decision
 -theoretic framework\, guaranteeing that the chosen design proceeds in a c
 oherent fashion. We propose a utility function based on the number of true
  positives which is straightforward to specify and intuitively appealing. 
 As for most sequential design problems\, an exact solution is computationa
 lly prohibitive. To address the computational challenge and also to limit 
 the dependence on an arbitrarily chosen utility function we propose instea
 d a simulation-based approximation with decision boundaries. The approach 
 allows us to determine good designs within reasonable computing time and i
 s characterized by intuitively appealing decision boundaries. We apply the
  method to next-generation sequencing\, microarray and reverse phase prote
 in array studies. We show that it can lead to substantial increases in pos
 terior expected utility. An implementation of the proposed approach is ava
 ilable in the Bioconductor package gaga.\n
LOCATION:Seminar Room 1\, Newton Institute
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