title: Two-stage designs for experiments with a large number of hypotheses.
abstract: BACKGROUND
  When a large number of hypotheses are investigated the false discovery rate (FDR) is commonly applied in gene expression analysis or gene association studies. Conventional single-stage designs may lack power due to low sample sizes for the individual hypotheses. We propose two-stage designs where the first stage is used to screen the 'promising' hypotheses which are further investigated at the second stage with an increased sample size. A multiple test procedure based on sequential individual P-values is proposed to control the FDR for the case of independent normal distributions with known variance.
  
  
  RESULTS
  The power of optimal two-stage designs is impressively larger than the power of the corresponding single-stage design with equal costs. Extensions to the case of unknown variances and correlated test statistics are investigated by simulations. Moreover, it is shown that the simple multiple test procedure using first stage data for screening purposes and deriving the test decisions only from second stage data is a very powerful option.
authors.affiliation: Section of Medical Statistics, Medical University of Vienna Spitalgasse 23, A-1090 Vienna, Austria.
authors.forename: Sonja
authors.initials: S
authors.lastname: Zehetmayer
authors.forename: Peter
authors.initials: P
authors.lastname: Bauer
authors.forename: Martin
authors.initials: M
authors.lastname: Posch
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical