Debiased Digital Twins for Generalizing Treatment Effects from Randomized Trials to Routine Care

Thomas M. Menino Convention & Exhibition Center 

Randomized trials provide high-quality causal evidence but often enroll selective populations. By contrast, electronic health records capture broader clinical populations but frequently lack the outcomes needed for reliable treatment-effect estimation. This limits the ability to extend high-quality evidence to routine-care settings when the target outcome is unavailable or incompletely measured. Here we propose a debiased digital twin framework for estimating treatment effects in target populations with covariate-only data. The framework learns outcome models in a source dataset, applies them to the target population to generate paired digital twins, and then uses bias reference outcomes (BROs), whose population-level treatment effects are expected to be null, to detect and calibrate residual bias arising from limited generalizability. The calibration step is a wrapper and can be readily adapted across data settings and digital twin models. In a real-world application transporting brain imaging-based evidence from SPRINT-MIND to a Penn Medicine EHR cohort, evaluation through leave-one-out BRO falsification analyses showed that naive digital twin estimates were systematically biased and severely undercovered, whereas BRO-calibrated estimates were well centered and achieved near-nominal coverage, improving from 2.2% to 95.2%. Across 238 white matter lesion outcomes, the debiased estimates preserved the overall protective pattern associated with intensive blood pressure treatment while enabling inference when the primary outcomes were not routinely observed in the target population. These results support BRO-calibrated digital twins as a practical approach for extending treatment-effect evidence from selective studies to broader clinical populations.

Causal Inference

Negative control outcome calibration/ Bias Reference Outcomes

Observational studies

Electronic health records 

Section on Statistics in Epidemiology