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Table 2 Important statistical quantities for reporting a clinical trial, and how they may be affected by an adaptive design

From: Adaptive designs in clinical trials: why use them, and how to run and report them

Statistical quantity Fixed-design RCT property Issue with adaptive design Potential solution
Effect estimate Unbiased: on average (across many trials) the effect estimate will have the same mean as the true value Estimated treatment effect using naive methods can be biased, with an incorrect mean value Use adjusted estimators that eliminate or reduce bias; use simulation to explore the extent of bias
Confidence interval Correct coverage: 95% CIs will on average contain the true effect 95% of the time CIs computed in the traditional way can have incorrect coverage Use improved CIs that have correct or closer to correct coverage levels; use simulation to explore the actual coverage
p value Well-calibrated: the nominal significance level used is equal to the type I error rate actually achieved p values calculated in the traditional way may not be well-calibrated, i.e. could be conservative or anti-conservative Use p values that have correct theoretical calibration; use simulation to explore the actual type I error rate of a design
  1. CI confidence interval, RCT randomised controlled trial