Leveraging real-world data to conduct externally controlled trial for rare diseases with count-type endpoints: utilizing multiple entries - a simulation study.

WHY IT MATTERS

If this method gets adopted by regulators, it could speed up approval timelines for rare disease treatments by allowing researchers to use more real-world patient data, potentially bringing new therapies to patients years sooner.

Scientists are testing a new way to run drug trials for rare diseases where there aren't enough patients. Instead of only comparing patients at one specific time point, this method allows researchers to use patient information from multiple different dates. This could make it easier and faster to test new medicines for rare diseases without needing as many people in the study.

Leveraging real-world data to conduct externally controlled trial for rare diseases with count-type endpoints: utilizing multiple entries - a simulation study. Abstract: Conducting randomized controlled trials for medications targeting rare diseases presents significant challenges, due to the scarcity of participants and ethical considerations. Under such circumstances, leveraging real-world data (RWD) to generate supporting evidence may be accepted by the regulatory agency. Constructing an external control arm (ECA) from RWD for a single-arm trial has been conducted occasionally. A complication in this design is that patients from RWD may be eligible at multiple time points. Most studies approach this by selecting one time point as the index date for ECA patients. Here, we propose a novel design for externally controlled trials that permits the inclusion of ECA patients at various entry points. Accompanying this design, we make recommendations for statist Authors: Sun et al. Journal: Journal of biopharmaceutical statistics MeSH: Humans, Rare Diseases, Computer Simulation, Endpoint Determination, Research Design, Randomized Controlled Trials as Topic, Sample Size, Models, Statistical, Data Interpretation, Statistical