  When evaluating the effectiveness of a drug, a Randomized Controlled Trial
(RCT) is often considered the gold standard due to its perfect randomization.
While RCT assures strong internal validity, its restricted external validity
poses challenges in extending treatment effects to the broader real-world
population due to possible heterogeneity in covariates. In this paper, we
introduce a procedure to generalize the RCT findings to the real-world
trial-eligible population based on the adaption of existing statistical
methods. We utilized the augmented inversed probability of sampling weighting
(AIPSW) estimator for the estimation and omitted variable bias framework to
assess the robustness of the estimate against the assumption violation caused
by potentially unmeasured confounders. We analyzed an RCT comparing the
effectiveness of lowering hypertension between Songling Xuemaikang Capsule
(SXC), a traditional Chinese medicine (TCM), and Losartan as an illustration.
The generalization results indicated that although SXC is less effective in
lowering blood pressure than Losartan on week 2, week 4, and week 6, there is
no statistically significant difference among the trial-eligible population at
week 8, and the generalization is robust against potential unmeasured
confounders.
