Estimating Individualized Treatment Effects in Acute Ischemic Stroke with Causal Transformation Models (TRAM-DAG): A Multi-Centre Observational Study with External RCT Validation

2026-06-10

AI Summary

This study addresses the limitation of relying solely on average treatment effects in acute ischemic stroke therapy by enabling precise estimation of individualized treatment effects. Leveraging the multicenter observational MAGIC dataset, the authors propose TRAM-DAG, a novel causal transformation model that integrates directed acyclic graphs, propensity score matching within the NIHSS ≥ 6 subgroup, and ordinal modeling of modified Rankin Scale (mRS) outcomes to estimate individual benefits of mechanical thrombectomy relative to thrombolysis. The model was externally validated in the MR CLEAN randomized controlled trial population, demonstrating alignment between its estimated average treatment effect and the trial’s observed results. Furthermore, TRAM-DAG effectively stratifies patients by their probability of favorable functional outcomes, offering robust support for personalized clinical decision-making.

Abstract

Personalized medicine in acute ischemic stroke requires moving beyond average treatment effects (ATE) to individualized treatment effect (ITE) estimates to support treatment decisions. In acute ischemic stroke, mechanical thrombectomy has been shown to be more effective on average than lysis in randomized controlled trials (RCTs), such as the MR CLEAN study. We aim to identify which individual patients benefit most from mechanical thrombectomy compared to lysis. The outcome of interest is the modified Rankin Scale (mRS) at three months, an ordinal measure of functional disability (0: no symptoms, 6: death). We demonstrate that causal transformation models on directed acyclic graphs (TRAM-DAG) can be used for ITE estimation after being fitted on observational MAGIC multi-center stroke patient data. To ensure comparability with the MR CLEAN population, which we use for validation, we train the TRAM-DAG on a MAGIC sub-population with NIHSS at admission >= 6, corresponding to one inclusion criterion of MR CLEAN. The fitted model is then used to estimate ITEs for stroke patients in the MR CLEAN population. While these ITE estimates cannot be confirmed experimentally, we show that their average is consistent with the trial's reported ATE. Furthermore, the ITE estimates correctly rank trial patients by their observed frequency of a good outcome (mRS at three months <= 2). These findings support the use of causal models like TRAM-DAG for personalized decision-making in stroke care and highlight their ability to bridge the gap between observational evidence and clinical trials.

Problem

• individualized treatment effects
• acute ischemic stroke
• mechanical thrombectomy
• personalized medicine
• treatment effect heterogeneity

Innovation

• individualized treatment effect
• causal transformation model
• TRAM-DAG
• observational study
• external RCT validation