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 statistical methods to account for measured confounders, limited sample size, within-subject correlation, and potential overdispersion inherent in count data. Furthermore, we present an idea for the blinding process for this type of study. We have conducted a series of simulations to assess the performance of the design and statistical methods in terms of bias, type I error, and efficiency, as compared to the approach of selecting only one entry per ECA patient. The study and parameter setup were based on a hypothetical case inspired by a rare disease study. The results indicate that allowing multiple entries for ECA patients can lead to enhanced performance in many aspects. It provides a controlled type I error, robustness against certain model misspecifications, and a moderate power improvement compared with selecting a single entry per ECA patient.