Speaker
Description
In adaptive clinical trials, the conventional point estimators of the treatment effect are prone to bias. Similarly, the conventional confidence intervals are prone to incorrect coverage, as well as other undesirable statistical properties. Recent regulatory guidance, such as ICH E20, has highlighted the need to use adjusted estimators and confidence intervals for adaptive designs in order to address these issues.
In this talk, we provide a comprehensive review of available methods for adaptive designs to 1) remove or reduce the potential bias in point estimators for adaptive designs and 2) construct confidence intervals with the desired coverage. We describe several classes of methodological techniques and provide a classification of adjusted estimators and confidence intervals by the type of adaptive design. We also highlight available software and code and discuss the remaining methodological gaps in the literature.
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