Introduction
ECAs
RARE DISEASES
In orphan and rare diseases, single-arm trials are common given the impracticability, if not impossibility, of randomized controlled trials [Numerous methods have been proposed to handle the potential confounders in ECAs. When individual-level data are available, frequentist approaches may estimate a causal effect using g-computation, propensity score (PS)-based weighting, PS-based stratification, PS-based matching, targeted maximum likelihood estimation (TMLE), etc. [By definition, a confounding variable must be correlated with the outcome and the treatment. The randomization aspect of many studies minimizes the correlations and hence the risk of bias due to confounding. However, when real-word data (RWD) is used to construct an ECA, the benefits of randomization are not possible, and unmeasured or unobserved predictive variables may represent unobserved confounders. Furthermore, the confounder misclassification may lead to another layer of bias when combining trial data and RWD [