Speaker
Description
Background: We consider clinical trials in which the experimental treatment may have heterogeneous effects across pre-specified patient subpopulations. In such settings, two-stage adaptive enrichment designs allow the enrolled population to be modified at an interim analysis. In stage 1, patients are enrolled from the full population, and based on interim data and preplanned selection rules, stage 2 enrolment may be restricted to subpopulations most likely to benefit. Because these interim decisions are data-dependent, valid statistical inference must account for the adaptation. While hypothesis testing and point estimation methods for adaptive enrichment designs have been well established, corresponding confidence interval methods remain limited. We focus on constructing confidence intervals for the treatment effect in the selected population.
Method: Confidence intervals that ignore population adaptation may fail to achieve nominal coverage. We propose a new approach that constructs confidence intervals with exact 100(1−α)% coverage conditional on the interim decision, ensuring that unconditional coverage is also exact. Our method applies to a broad class of adaptive enrichment designs. Given the interim selection, we derive the conditional distribution of the naive treatment effect estimator and invert uniformly most powerful unbiased tests to obtain the uniformly most accurate unbiased confidence interval. An efficient computational procedure is provided.
Results: We conduct extensive simulations which confirm that the proposed intervals achieve the desired conditional coverage with moderate width inflation compared to the standard confidence interval.
Contribution: This approach offers a rigorous framework for post-selection inference in adaptive clinical trials and contributes to the development of statistically principled adaptive design methodology.
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