Selected article for: "box plot and lower end"

Author: Qingyang Xu; Shomesh Chaudhuri; Danying Xiao; Andrew W Lo
Title: Bayesian Adaptive Clinical Trials for Anti-Infective Therapeutics during Epidemic Outbreaks
  • Document date: 2020_4_14
  • ID: 20hk99h4_87
    Snippet: Page 15 of 27 All Rights Reserved Figure 3 . Sensitivity of optimal sample size (in each arm of non-adaptive Bayesian RCT) to the weekly subject enrollment rate . From the lower to upper end of each box plot, the five-parameter summary corresponds to [50%, 75%, 100%, 125%, 150%] of the baseline value used in our analysis (Table 1 ). Figure 4 . Sensitivity of optimal Type I error rate to the weekly subject enrollment rate . From the upper to lower.....
    Document: Page 15 of 27 All Rights Reserved Figure 3 . Sensitivity of optimal sample size (in each arm of non-adaptive Bayesian RCT) to the weekly subject enrollment rate . From the lower to upper end of each box plot, the five-parameter summary corresponds to [50%, 75%, 100%, 125%, 150%] of the baseline value used in our analysis (Table 1 ). Figure 4 . Sensitivity of optimal Type I error rate to the weekly subject enrollment rate . From the upper to lower end of each box plot, the five-parameter summary corresponds to [50%, 75%, 100%, 125%, 150%] of the baseline value used in our analysis (Table 1) .

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