A confounder debiasing method for RCT-like comparability enables Machine Learning-based personalization of survival benefit in living donor liver transplantation

Many clinical questions in medicine cannot be answered through randomized controlled trials (RCTs) due to ethical or feasibility constraints. In such cases, observational data is often the only available resource for evaluating treatment effects. To address this challenge, we have developed Decision Path Similarity Matching (DPSM), a novel machine learning (ML)-based algorithm that simulates RCT-like conditions to debias observational data. In this study, we apply DPSM to the clinical question of living donor liver transplantation (LDLT) versus deceased donor liver transplantation (DDLT), helping to identify which patients benefit most from LDLT. DPSM leverages decision paths from a Random Forest classifier to perform accurate, one-to-one matching between LDLT and DDLT recipients, minimizing confounding while retaining interpretability. Using data from the Scientific Registry of Transplant Recipients (SRTR), including 4,473 LDLT and 68,108 DDLT patients transplanted between 2002 and 2023, we trained independent Random Survival Forest (RSF) models on the matched cohorts to predict post-transplant survival. DPSM successfully reduced confounding associations between the two groups as shown by a decrease in area under the receiver operating characteristic (AUROC) from 0.82 to 0.51. Subsequently, RSF (C-indexldlt=0.67, C-indexddlt=0.74) outperformed the traditional Cox model (C-indexldlt=0.57, C-indexddlt=0.65). The predicted 10-year mean survival gain was 10.3% (SD = 5.7%). In conclusion, DPSM provides an effective approach for creating RCT-like comparability from observational data, enabling personalized survival predictions. By leveraging real-world data where RCTs are impractical, this method offers clinicians a tool for transitioning from population-level evidence to more nuanced, personalization.