Leveraging External Data in Rare Disease Trials Through Individualized Discounting Within the Power Prior: A Case Study in Hereditary Angioedema
提出一种新的统计方法（DIPP），通过个体化折扣参数在罕见病临床试验中整合外部数据，解决数据不可交换性问题，并用遗传性血管性水肿案例验证其有效性。
An increasingly common approach in clinical trials involves leveraging external data to supplement the sample size of randomized controlled trials. Although designing studies with concurrent data allows direct treatment comparisons, recruiting an adequate number of participants, especially in the context of rare diseases, can be challenging, time-intensive, and financially burdensome. Incorporating external data offers a cost-effective solution to enhance trial data; however, potential non-exchangeability between external and current data introduces significant challenges. To address this, we propose the Discounting Individuals Power Prior (DIPP), a novel extension of the power prior methodology. The DIPP introduces an individual-specific local discounting parameter alongside a global discounting parameter. It employs a spike-and-slab type prior, where the probability is tied to the assumed degree of exchangeability between external and concurrent trial data, with this degree treated as a random variable. We evaluate the DIPP’s performance through comparisons with the power prior, the normalized power prior, and a propensity score-based approach in simulation studies. Furthermore, we demonstrate its utility in a detailed case study of real-world clinical trial data for an inherited disorder. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.