Therapeutic innovation & regulatory science · 2026
The Role of Digital Twins in Clinical Trials: A New Perspective on Methodological and Statistical Challenges.
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Cicin Fatma Nur
Аннотация
BACKGROUND: Randomized controlled trials (RCTs) remain the gold standard for evaluating medical interventions, yet they face persistent methodological challenges: patient dropout, inadequate sample sizes, premature termination, missing data, and the limitations of indirect treatment comparisons. The emergence of digital twin technology-computational patient representations that generate simulated clinical trajectories statistically indistinguishable from real-world data-offers a promising methodological complement to address these limitations.
APPROACH: We synthesize evidence from key methodological literature, regulatory guidance (ICH E9(R1), FDA, EMA), and proof-of-concept studies across oncology, neurology, and Alzheimer's disease. A taxonomy of in silico clinical trial approaches provides the organizational framework. This article is a narrative perspective rather than a systematic review; no predefined search strategy, database range, or inclusion/exclusion criteria were applied.
RESULTS: Digital twins may provide complementary methodological approaches across nine trial challenges: model-based sensitivity analyses for missing (MNAR) outcomes; Bayesian and posterior predictive modeling of partially observed longitudinal outcomes; prognostic covariate adjustment and virtual augmentation to improve efficiency; prospective simulation of futility stopping rules; prospective calibration of efficacy stopping boundaries; individualized control-arm (ICA) construction; hybrid augmented and synthetic control strategies; pre-trial protocol optimization through in silico simulation; and population-adjusted indirect treatment comparison.
CONCLUSIONS: Digital twins represent an emerging methodological ecosystem rather than isolated tools. Their methodological potential depends on consistent validation and explainability standards; frameworks for detecting algorithmic bias; clarification of regulatory acceptance criteria; and comprehensive data infrastructure addressing representativeness gaps. Digital twin approaches should be viewed as complementary to-not replacements for-established randomized trial methodology.
APPROACH: We synthesize evidence from key methodological literature, regulatory guidance (ICH E9(R1), FDA, EMA), and proof-of-concept studies across oncology, neurology, and Alzheimer's disease. A taxonomy of in silico clinical trial approaches provides the organizational framework. This article is a narrative perspective rather than a systematic review; no predefined search strategy, database range, or inclusion/exclusion criteria were applied.
RESULTS: Digital twins may provide complementary methodological approaches across nine trial challenges: model-based sensitivity analyses for missing (MNAR) outcomes; Bayesian and posterior predictive modeling of partially observed longitudinal outcomes; prognostic covariate adjustment and virtual augmentation to improve efficiency; prospective simulation of futility stopping rules; prospective calibration of efficacy stopping boundaries; individualized control-arm (ICA) construction; hybrid augmented and synthetic control strategies; pre-trial protocol optimization through in silico simulation; and population-adjusted indirect treatment comparison.
CONCLUSIONS: Digital twins represent an emerging methodological ecosystem rather than isolated tools. Their methodological potential depends on consistent validation and explainability standards; frameworks for detecting algorithmic bias; clarification of regulatory acceptance criteria; and comprehensive data infrastructure addressing representativeness gaps. Digital twin approaches should be viewed as complementary to-not replacements for-established randomized trial methodology.