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

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.