Derives a unified UMVCUE for general subpopulation selection rules in adaptive enrichment designs based on sample space partition.
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A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
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Unbiased estimation in two-stage adaptive enrichment designs
Derives a unified UMVCUE for general subpopulation selection rules in adaptive enrichment designs based on sample space partition.
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In-Sample Evaluation of Subgroups Identified by Generic Machine Learning
A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.