Leveraging Real-World Data in COVID-19 Response

Abstract Starting in early 2020, a fast-ravaging viral infection erupted and caused the COVID-19 (coronavirus disease of 2019) pandemic. The disease rapidly spread across the world and has altered people’s lifestyle since its first reporting. Many scientists and medical practitioners have strived to understand the disease and research for treatments and vaccines. As real-world data quickly accumulate, the general public reacts to new findings and government bodies enforce preventive measures accordingly. These actions subsequently alter the real-world data pattern and structure. It creates great challenges in interpreting this maze of data. This article delves into the specificity of COVID-19 real-world data; summarizes some existing COVID-19 databases and the disease modeling strategies; outlines potential trial designs incorporating real-world data to meet evidentiary requirements for treatment effect demonstration; and then presents a few case examples. It provides statistical considerations for real-world data utilization in understanding COVID-19 and finding potential treatments and preventive care.