Digital twins are increasingly discussed in drug development, often narrowly as virtual control arms or synthetic data. We propose a broader framework for data-driven clinical digital twins, organized into five levels according to how much of the trial data matrix of baseline covariates, treatment, and responses is generated by a statistical or machine-learning model trained on historical, external, or real-world data. The levels are: (1) prognostic score adjustment, (2) response trajectory prediction, (3) virtual control, (4) synthetic population, and (5) fully synthetic patients and responses. For each level we describe the estimation workflow, the development decision it can support, and the principal statistical guardrail. As the share of model-generated information increases, the inferential weight placed on the model grows and regulatory maturity generally declines. We argue that credibility requirements should therefore scale with context of use, characterized jointly by model influence and decision consequence, following a risk-based credibility framework. We identify four recurring credibility questions (drift and transportability, estimand alignment, compounding error, and data quality and provenance), summarize the current regulator landscape, and discuss bottlenecks in data, model transferability, collaboration, and lifecycle governance. The central message for statisticians is to start from the decision rather than the model, and to earn trust use by use.

翻译：暂无翻译