Abstract:
|
Randomized controlled clinical trials (RCTs) are the gold standard for evaluating the safety and efficacy of pharmaceutical drugs, but in many cases their costs, duration, limited generalizability, and ethical or technical feasibility have caused some to look for real-world studies as alternatives. On the other hand, real-world studies may be much less convincing due to the lack of randomization and the presence of confounding bias. To derive robust real-world evidence (RWE) from the analysis of the real-world data (RWD) from real-world studies, we consider the targeted learning causal-inference roadmap. In this presentation, we will demonstrate the application of the causal-inference roadmap to some simulated real-world studies. Three key steps in the roadmap are (1) define a target estimand that aligns with the research objective; (2) select an efficient estimator for estimating the target estimand and an estimator of its uncertainty; (3) evaluate the robustness of conclusions to violations of untestable causal assumptions. This demonstration shows how the causal-inference roadmap can be used to analyze RWD to generate RWE.
|