Study design
We generated a treatment arm (e.g., single-arm trial) and an ECA (e.g., standard of care) in order to simulate an externally controlled study. Three types of outcomes – continuous, binary, and time-to-event endpoints were assessed between two arms. In order to emulate a biased ECA, both measured and unmeasured confounders were added to the data. For each outcome, we explored three scenarios for the relationship between unmeasured confounders and observed variables (Fig. Diagram of measured and unmeasured confoundersIn scenario 1 & 2, as shown in Fig. In scenario 3 (Fig. A set of random samples were drawn from a simulated target population for each scenario. For each generated sample, g-computation, PS-based weighting, e.g., inverse probability of treatment weighting (IPTW), standardized mortality/morbidity ratio (SMR), and overlap weighting (OW), and targeted maximum likelihood estimation (TMLE) were used to estimate treatment effects based on observed outcomes and measured confounders which were incorporated in the analytic model.