As a novel research model that can address multiple research questions within an overall trial structure, master protocol design shares similarities with the clinical research on syndrome-based traditional Chinese medicine in terms of study design. The sample size estimation in master protocol design is characterized by analyzing the subtrials separately and re-estimation at interim analyses. Specific methods include the combination of Simon’s two-stage design and Bayesian hierarchical design that facilitates information borrowing. By drawing on these methods to estimate dynamically and adjust the sample size for each subtrial in a targeted manner, it is expected to provide a feasible approach for the methodological development of sample size estimation in the field of clinical research on syndrome-based traditional Chinese medicine.
Traditional Chinese medicine (TCM) emphasizes syndrome differentiation and individualized interventions. However, in real-world clinical studies, complex confounding and the unobservability of counterfactual outcomes pose major challenges for evaluating individualized treatment effects. Causal machine learning, grounded in the potential outcomes framework, provides a flexible, nonparametric approach to model treatment assignment and outcome distributions. By adjusting for baseline and time-varying confounding, it enables the generation of patient-specific counterfactual outcomes and more accurate estimation of causal effects at the individual level. This methodological advance addresses the limitations of conventional approaches that focus mainly on average treatment effects. Taking advanced colorectal cancer as a case example, we demonstrate how causal machine learning can identify "which patients benefit most from which treatment?" thereby supporting personalized clinical decision-making and optimization of therapeutic strategies in TCM. Overall, causal machine learning holds both methodological significance and direct clinical value for individualized efficacy research in TCM.