Literature DB >> 17463029

Two-stage designs applying methods differing in costs.

Alexandra Goll1, Peter Bauer.   

Abstract

MOTIVATION: Two-stage pilot and integrated designs are powerful tools for investigating large numbers of hypotheses. Asymptotically, optimal two-stage designs controlling the familywise error or false discovery rate are considered when costs and effect sizes per measurement differ between stages and total costs are constrained.
RESULTS: Depending on the cost and effect size ratios between the measurements, it is generally more powerful to apply two-stage procedures using one measurement method at both stages. For the practically relevant case that the same method is applied at both stages but designing the second-stage measurements raises extra costs, two-stage designs are more powerful than the single-stage design even for large costs ratios. The power of the optimal pilot and integrated two-stage designs generally are similar, however, the integrated approach is less sensitive even to severe design misspecifications in the planning phase. AVAILABILITY: R-programs (R, 2005) to calculate asymptotically optimal designs are available on: http://statistics.msi.meduniwien.ac.at/index.php?page=ao2stage

Mesh:

Year:  2007        PMID: 17463029     DOI: 10.1093/bioinformatics/btm140

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  5 in total

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Journal:  Stat Biosci       Date:  2017-02-10

2.  Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls.

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3.  Robust joint analysis allowing for model uncertainty in two-stage genetic association studies.

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Journal:  BMC Bioinformatics       Date:  2011-01-07       Impact factor: 3.169

4.  Biomarker discovery for heterogeneous diseases.

Authors:  Garrick Wallstrom; Karen S Anderson; Joshua LaBaer
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2013-03-05       Impact factor: 4.254

5.  False discovery rate control in two-stage designs.

Authors:  Sonja Zehetmayer; Martin Posch
Journal:  BMC Bioinformatics       Date:  2012-05-06       Impact factor: 3.169

  5 in total

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