A model-agnostic conformal selection method reformulates CATE-based beneficiary identification as multiple testing with RCT-calibrated p-values and FDR control, allowing external data for model training.
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2 Pith papers cite this work. Polarity classification is still indexing.
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The paper introduces a question-driven framework and set of statistical methods for exploratory assessment of regional treatment effect heterogeneity in multi-regional clinical trials, evaluated via simulations under no-heterogeneity and modifier-driven scenarios.
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A Conformal Selection Framework for Individual Treatment Beneficiaries with Auxiliary External Data
A model-agnostic conformal selection method reformulates CATE-based beneficiary identification as multiple testing with RCT-calibrated p-values and FDR control, allowing external data for model training.
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A Workflow for Evaluating Regional Treatment Effect Heterogeneity in Multi-Regional Clinical Trials
The paper introduces a question-driven framework and set of statistical methods for exploratory assessment of regional treatment effect heterogeneity in multi-regional clinical trials, evaluated via simulations under no-heterogeneity and modifier-driven scenarios.