A Conformal Selection Framework for Individual Treatment Beneficiaries with Auxiliary External Data

2026-06-30

AI Summary

This study addresses the challenge of precisely identifying individual patients who are likely to benefit from a specific treatment while rigorously accounting for uncertainty quantification and multiplicity correction. The authors reformulate the conditional average treatment effect (CATE)-driven patient selection problem within a multiple hypothesis testing framework, leveraging randomized controlled trial (RCT) data to construct conformal p-values and applying the Benjamini–Hochberg procedure to control the false discovery rate (FDR). To enhance estimation efficiency, real-world data are further integrated into the analysis. The key innovation lies in the first-time integration of conformal inference with FDR control for individualized treatment selection, achieving a balance between statistical reliability and model performance under limited RCT sample sizes. Empirical results validate effective FDR control and successfully identify a subgroup of early-stage non-small cell lung cancer patients who benefit from limited resection, thereby helping to mitigate overtreatment.

Abstract

Identifying patients who are likely to benefit from a treatment is central to precision medicine and can guide follow-up trials, enrichment designs, and individualized decisions. Although randomized controlled trials (RCTs) provide evidence on efficacy, they are usually powered to estimate average treatment effects rather than patient-level benefit. Meanwhile, artificial intelligence and machine learning methods offer flexible tools for estimating heterogeneous treatment effects, especially when augmented by real-world data (RWD). However, in practice, these estimated effects are often translated into decisions through simple ranking or thresholding rules, which can ignore uncertainty and multiplicity when many patients are evaluated simultaneously. Motivated by this, we propose a model-agnostic conformal inference framework for uncertainty-aware beneficiary selection. The framework reformulates CATE-based treatment-benefit selection as a multiple-testing problem. For each candidate, we test whether the conditional treatment benefit exceeds a clinically meaningful threshold and construct a conformal p-value using RCT-based calibration. These p-values are then adjusted by the Benjamini-Hochberg procedure to control the false discovery rate (FDR) among selected beneficiaries. To improve efficiency when RCT sample sizes are limited, external data, such as RWD, can be used to train flexible treatment effect models, while conformal calibration remains anchored in the RCT data. It can be paired with conventional machine learning algorithms and emerging tabular foundation models. Simulations show that the framework maintains FDR control, with power depending on the base learner and external-data comparability. A case study in early-stage non-small-cell lung cancer illustrates how the method identifies candidate profiles with evidence of benefit from limited resection to reduce overtreatment.

Problem

• individual treatment benefit
• heterogeneous treatment effects
• false discovery rate
• conformal inference
• precision medicine

Innovation

• conformal inference
• heterogeneous treatment effects
• false discovery rate
• real-world data
• precision medicine