Selective randomization inference for subgroup effects with continuous biomarkers

2025/04/27 by Zijun Gao, Gao, Zijun
Biochemistry, Genetics and Molecular Biology · Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #FOS: Computer and information sciences #Genetic Associations and Epidemiology #Methodology (stat.ME) #Statistical Methods in Clinical Trials

paper · pdf · doi:10.48550/arxiv.2504.19380

openalex publication_date 2025/04/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Randomization tests are a popular method for testing causal effects in clinical trials with finite-sample validity. In the presence of heterogeneous treatment effects, it is often of interest to select a subgroup that benefits from the treatment, frequently by choosing a cutoff for a continuous biomarker. However, selecting the cutoff and testing the effect on the same data may fail to control the type I error. To address this, we propose using "self-contained" methods for selecting biomarker-based subgroups (cutoffs) and applying conditioning to construct valid randomization tests for the subgroup effect. Compared to sample-splitting-based randomization tests, our proposal is fully deterministic, uses the entire selected subgroup for inference, and is thus more powerful. Moreover, we demonstrate scenarios where our procedure achieves power comparable to a randomization test with oracle knowledge of the benefiting subgroup. In addition, our procedure is as computationally efficient as standard randomization tests. Empirically, we illustrate the effectiveness of our method on simulated datasets and the German Breast Cancer Study.

Citations

• Selective randomization inference for adaptive experiments
• Randomization Inference beyond the Sharp Null: Bounded Null Hypotheses and Quantiles of Individual Treatment Effects
• Orthogonal Statistical Learning
• Quasi-Oracle Estimation of Heterogeneous Treatment Effects
• Quasi-oracle estimation of heterogeneous treatment effects
• Uniformly valid confidence intervals post-model-selection
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Selective inference with a randomized response
• Recursive Partitioning for Heterogeneous Causal Effects
• A paradox from randomization-based causal inference
• Valid post-selection inference
• Anti-concentration and honest, adaptive confidence bands
• The Hazards of Hazard Ratios
• Estimating causal effects of treatments in randomized and nonrandomized studies.

Related

• Adaptive sample splitting for randomization tests
• Sharp Inference on Selected Subgroups in Observational Studies
• Chiseling: Powerful and Valid Subgroup Selection via Interactive Machine Learning
• Subgroup analysis of treatment effects for misclassified biomarkers with time-to-event data
• ASIED: A Bayesian Adaptive Subgroup-Identification Enrichment Design