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.
The authors wish to express their gratitude to Sharon Browning (Department of Statistics, University of Washington, Seattle), Rohan Ameratunga and Wikke Koopman (Lab PLUS, Auckland District Health Board, New Zealand), Patrick Gladding (North Shore Hospital, Auckland, New Zealand) and Jocelyne Benatar (Cardiac Vascular Research Unit, Auckland City Hospital) for useful discussions and involvement in the cardiac and immunology proteomics studies. The authors wish to acknowledge the financial support from Green Lane Research and Education Trust and A+ Charitable Trust for the cardiac and immunology proteomic studies. The authors also wish to acknowledge the contribution of the NeSI high-performance computing facilities and the staff at the Centre for eResearch at the University of Auckland (Gene Soudlenkov and Sina Masoud-Ansari). New Zealand’s national facilities are provided by the New Zealand eScience Infrastructure (NeSI) and funded jointly by NeSI’s collaborator institutions and through the Ministry of Business, Innovation and Employment’s Infrastructure programs (URL: http://www.nesi.org.nz). The authors also acknowledge the associate editor and two anonymous reviewers for their constructive comments.
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