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Two optimization strategies of multi-stage design in clinical proteomic studies

Author

Listed:
  • Zeng Irene S.L.

    (Department of Statistics, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand)

  • Lumley Thomas

    (Department of Statistics, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand)

  • Ruggiero Kathy

    (Department of Statistics, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand)

  • Middleditch Martin

    (Centre for Genomics and Proteomics, School of Biological Sciences and Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand)

  • Woon See-Tarn

    (Lab PLUS, Virology and Immunology, LabPLUS, PO Box 110031, Auckland City Hospital, Auckland 1148, New Zealand)

  • Stewart Ralph A.H.

    (School of Medicine, University of Auckland and Green Lane Cardiac Service, PO Box 110031, Auckland City Hospital, Auckland 1148, New Zealand)

Abstract

We evaluated statistical approaches to facilitate and improve multi-stage designs for clinical proteomic studies which plan to transit from laboratory discovery to clinical utility. To find the design with the greatest expected number of true discoveries under constraints on cost and false discovery, the operating characteristics of the multi-stage study were optimized as a function of sample sizes and nominal type-I error rates at each stage. A nested simulated annealing algorithm was used to find the best solution in the bounded spaces constructed by multiple design parameters. This approach is demonstrated to be feasible and lead to efficient designs. The use of biological grouping information in the study design was also investigated using synthetic datasets based on a cardiac proteomic study, and an actual dataset from a clinical immunology proteomic study. When different protein patterns presented, performance improved when the grouping was informative, with little loss in performance when the grouping was uninformative.

Suggested Citation

  • Zeng Irene S.L. & Lumley Thomas & Ruggiero Kathy & Middleditch Martin & Woon See-Tarn & Stewart Ralph A.H., 2013. "Two optimization strategies of multi-stage design in clinical proteomic studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(2), pages 263-283.
    • Handle: RePEc:bpj:sagmbi:v:12:y:2013:i:2:p:263-283:n:1007
    DOI: 10.1515/sagmb-2013-0005
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