[Virtual efficacy evaluation: methodological framework linking randomized controlled trials and real-world evidence...

In clinical research on TCM, RCT remains the key way to obtain evidence of causal effect. However, the implementation is often constrained by factors such as resource input, syndrome heterogeneity, and long-term outcome observation, so that single trials rarely provide sufficient support for clinical decision-making and entail higher trial-and-error costs. With the rapid accumulation of RWD in China and ongoing advances in causal inference and modeling methods internationally, there is an urgent need for a systematic methodological framework for TCM and other complex interventions that can shift part of this costly trial-and-error process from conventional clinical trial paths and expand the theoretical and technical space for efficacy evaluation of TCM. The concept of VEE was proposed and defined as a methodological framework that, when RCT cannot be adequately, promptly or widely conducted, or when existing trials cannot answer all key clinical questions, based on RWD, the virtual modeling techniques such as causal inference, digital-twin, and artificial intelligence was used to reconstruct intervention processes in a model space according to clinical trial logic and to simulate the causal effects of TCM interventions and their associated uncertainties. On this basis, a four-layer framework comprising a data foundation layer, causal identification layer, virtual modeling layer, and inferential application layer was constructed. The paper systematically clarified how VEE, under a target-trial perspective, integrated multi-source data, defined estimable effects, and reconstructed virtual disease courses and treatment pathways, and how it can be embedded into key sections such as trial question formulation, design optimization, result interpretation, and individualized decision-making, thereby providing methodological support to maximally reduce trial-and-error risk in physical clinical studies and to improve the clinical research efficiency and evidence continuity of TCM.