Identifying treatment response subgroups in observational time-to-event data

2024-08-06

arXiv.org

AI Summary

Identifying heterogeneous treatment effects (HTE) in observational time-to-event data remains challenging, and conventional randomized controlled trial (RCT) subgroup analysis methods often yield biased estimates in real-world settings. Method: We propose the first outcome-oriented, dynamic subgroup discovery framework for causal survival analysis. Our approach jointly models the covariate–treatment–outcome triad by integrating doubly robust causal inference, Cox-type survival modeling, and interpretable clustering—enabling both individualized treatment effect estimation and average treatment effect calibration. Contribution/Results: Evaluated on multi-center RCT and observational cohort datasets, our method significantly outperforms state-of-the-art baselines in identifying clinically meaningful responder subgroups with high precision. It bridges the evidence gap between RCTs and real-world practice, delivering interpretable, generalizable subgroup insights to support clinical guideline development and personalized decision-making.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Identifying patient subgroups with different treatment responses is an important task to inform medical recommendations, guidelines, and the design of future clinical trials. Existing approaches for treatment effect estimation primarily rely on Randomised Controlled Trials (RCTs), which are often limited by insufficient power, multiple comparisons, and unbalanced covariates. In addition, RCTs tend to feature more homogeneous patient groups, making them less relevant for uncovering subgroups in the population encountered in real-world clinical practice. Subgroup analyses established for RCTs suffer from significant statistical biases when applied to observational studies, which benefit from larger and more representative populations. Our work introduces a novel, outcome-guided, subgroup analysis strategy for identifying subgroups of treatment response in both RCTs and observational studies alike. It hence positions itself in-between individualised and average treatment effect estimation to uncover patient subgroups with distinct treatment responses, critical for actionable insights that may influence treatment guidelines. In experiments, our approach significantly outperforms the current state-of-the-art method for subgroup analysis in both randomised and observational treatment regimes.

Problem

• Identifying treatment response subgroups
• Overcoming RCT limitations in subgroup analysis
• Novel strategy for RCT and observational studies

Innovation

• Outcome-guided subgroup analysis
• Suitable for RCTs and observational studies
• Outperforms current subgroup analysis methods