All Times EDT
Keywords: biostatistics, clinical trial design, RWE, RWD, real-world evidence, real-world data, causal inference, causal inference framework, regulatory, novel data
A surging number of available real-world data (RWD) sources (e.g., EHRs, claims data, registries) have attracted many experts from multiple disciplines to actively exploring and developing ways to translate RWD into Real-World Evidence (RWE) to bridge the current gaps in clinical trial enterprise. The ASA BIOP Section chartered its RWE Scientific Working Group (SWG) in 2018 to facilitate using statistics to generate RWE designed to inform regulatory decisions of medical products. In its first phase, the SWG was divided into two workstreams and both reviewed biostatistical methods related to RWE generation for two different regulatory purposes: (I) to modify existing labels of medical products and (II) to inform better clinical study designs and analyses using external control. Both Workstreams have summarized their findings in three papers, which are submitted for publication. This presentation will discuss one of the three SWG papers, focusing on the biostatistical landscape of causal inference frameworks that support RWE clinical studies. First, some fundamental concepts that are relevant to the SWG's research scope will be defined. Then, a general causal inference framework will be used to describe how apparently separate and different causal inference methods do relate to each other as all play significant roles at various steps of a methodological roadmap, which is designed to help investigators to design, conduct, analyze, and interpret causal inference studies, including studies that generate RWE. Some remarks and potential topics that the SWG has identified for its future research will conclude the presentation.