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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